ML

2024-YR4-Asteroid

I full think that even in a total crash nvidia will say up.. they make the chips that more and more of everything runs on. Need complex calculations? Nvidia. Want to play games cause life sucks? Nvidia. Need to run an AI? Nvidia. Need ML programs for literally anything? Nvidia. IOT? Somehow, nvidia.

drankleanonce

after this week i really might just voo n chill. i made my money back from mexico ML VIVA MEXICO 🇲🇽

Yazzer5

let me drop the buzzwords completely. Here's what it actually is. Inputs: standard market features — price returns, realised volatility, RSI, moving average ratio, VIX level. Any OHLCV data works. What it does: instead of encoding those features into a classical ML model directly, it first runs them through a mathematical transformation called a quantum feature map. This maps your market data into a higher-dimensional space where regime boundaries are easier to separate. Then a standard SVM classifier makes the call. Outputs: current regime (bull / bear / transitioning), confidence score, and how many candles the signal leads the classical confirmation. Why not just use classical ML: a classical kernel does a similar thing but can only approximate certain feature interactions. The quantum feature map captures correlations between your inputs that classical kernels miss — specifically the kind of non-linear entangled relationships that show up at regime transitions. HSBC tested this on 1.1 million real bond trades and got 34% better prediction than classical ML alone. No repo yet building it today. But the architecture is straightforward: PennyLane for the feature map, sklearn SVM for classification, yfinance for data. Would you actually be willing to test it if I had something running by end of day?

Yazzer5

we're not using quantum hardware. Think of it this way: quantum computing gives us a new way to transform data before feeding it into classical ML. That transformation runs on a normal CPU via a simulator. No quantum chip required.

Yazzer5

You're right that without evidence it's just noise. what would "mathematically prove it works" look like to you? For what it's worth there is peer-reviewed work showing quantum kernel methods outperforming classical baselines on financial classification tasks not hype, actual AUC comparisons on real market data. HSBC and IBM published results in September 2025 showing 34% improvement over classical ML on bond trading using quantum feature maps on a simulator-reproducible methodology.

eat_my_ass_n_balls

Grok is the shittiest model and fundamentally unserious - it is not used by self-respecting ML people. It’s a shitposting model. Take that into consideration.

ChloroVstheWorld

easiest ML of my life hopefully

confused_teabagger

More ironically it anagrams to Am ML Satan!

Serialconsultant

I lead AI at a global consultancy firm. We worked with a global insurance company and reduced a complex global policy renewal process from 90 days (it wasn’t fully manual, leveraged RPAs and ML) via 7 AI Agents to make it done in under 7. All the human data validation, extraction, audit, linkage - run by agents. The first PoC converted a $17M process to a $6M process with the added consumption costs. LLMs host a dynamic rules engine which runs real time checks on the regulations. Anyone who says AI output isn’t realized is a sheep

PaperHandsTheDip

Well - it could have impacts on things like quantum. If we understand how the universe works we can abuse that to our advantage. It's like how some ML algorithms have found "short circuits / bugs" in the hardware that they're running on. Those are simulations. They were trained to solve a problem without direction. And they figured out that there was current leaking between different parts of the hardware that could be abused.

Mminas

GenAI will revolutionize digital humanities. There is too much work to be done and too few people to do it and areas like content enrichment, addressing the language barrier, accessibility, assisted curation will boom. Of course there is going to be a lot of low quality efforts but at the other side of the tunnel GenAI will help bring knowledge to the public in a way similar to how the Web first did in the nineties. As long as best practices and RAG are done properly GenAI is ridiculously efficient in content management. As for ML it is already revolutionizing practically every industrial process both in terms of automation and in terms of optimization and it is completely independent of the Chatbot hype/fad. Yes many effort will be crushed by realty like a bawl of eggs like back in the .com days, but the tech is here to stay.

Responsible-Meringue

Any insight to what's going to raise from the ashes after this insane hype cycle is over?  I've been professionally orthogonal to classical ML, and riding alongside the LLM explosion since '18. But I really don't know applications outside my subject matter focus (molecular modeling).  My science brain want specialized predictice infrence modles for biological graph networks. We probably don't have enough public data but whatever. 

UltraviolentLemur

"Best training data there is" – almost got me to get serious here with that ML rage bait. Nice try.

Busy_Bee_1729

The idea is that your fraud detection software will now be able to turn transaction data into a paragraph that's readable by humans with the main points summarised. They can also convert text instructions into commands. Whether you want it call ML or AI with LLM is up to you. It's all just different kinds of statistical analysis on different datasets at the end of the day.

Pitiful-Target-3094

Arguably fraud detection is more ML than AI with LLM?

biglebowskidude

CRWV - CoreWeave Senior Analyst Breakdown Recent IPO, NaN price data so no valuation — but here's the business breakdown: What they do: GPU cloud infrastructure for AI/ML workloads. Think AWS but specialized entirely for NVIDIA GPUs. Right place, right time. Bull case: • Perfect timing in Al infrastructure boom • 60-75% gross margins on GPU rental • Long-term contracts provide revenue visibility • Specialization beats generalist hyperscalers on GPU-specific pricing Bear case: • 100% dependent on NVIDIA GPU supply — single point of failure • AWS/Azure/GCP are aggressively adding GPU capacity • Massive capex requirements, pre-profitability • Customer concentration among AI startups (funding-dependent) The core risk: When NVIDIA supply normalizes and hyperscalers catch up on GPU capacity, CoreWeave's pricing power evaporates. They need to lock in long-term contracts NOW while the window is open. Verdict: High risk, high reward infrastructure play. Timing is everything — this is a 2-3 year window before competition intensifies significantly.

ChloroVstheWorld

Knicks ML will save us

EightFolding

Good question. Is AI profitable yet? Absolutely not. But the build-out alone toward that day is going to be a transformative generator of wealth for people who have been and are invested. Sovereign AI is a part of that, nations need AI infrastructure. The same is true of Quantum. Look at China's spending on quantum, the US is barely just beginning to think about catching up with the recent announcement. At the end of the day the same forces that are driving AI built-out and adoption across business and government will apply even if only because of encryption. Up until a few months ago most people would have said MU wasn't worth holding long-term due to the cyclical nature of the sector. I've held NVDA for years, because I was working with scientists a decade ago who were doing ML-based space science work and couldn't get enough GPUs due to competition with gaming. I've held MU for years because they were making the RAM in devices I used. In neither case did I think they would become central for a global AI revolution. So yeah, it's hard to say what might happen. But I think it's pretty clear we either end up with a post-classical computing revolution that amplifies the AI revolution in progress and they feed into each other, or we're all standing around a barrel fire talking about back when there was an internet. There's no in-between future IMO.

PurifyingProteins

Why the hostility? I know this is Reddit and it’s often a waste of time to provide an example to help someone see that maybe their view of a situation is more limited than they may realize, but I’m sure we can both see that others agree with my stance. I’m not trying to polish the knob of people selling AI/ML as the solution to everything, everywhere, and always. It’s often overkill with respect to resource utilization for easy tasks and underperforms for tasks it’s not well trained for but sold to be good at. However, with respect to its impact to the field of protein engineering for structurally biology and drug discovery applications, saying that “it’s pretty fucking great at suggesting which of my ideas is more likely to succeed” is such an understatement.

PurifyingProteins

Maybe it’s possible that their work and my work and other people’s work differ considerably from your’s. Some of the software tools that we utilize that either directly use ML models to generate data to solve problems, or they analyze the models output data and meta data to solve problems. To arrive at the same quality solutions without AI/ML would take anywhere from “100x longer”, to, “we would never arrive at it”, because we as a species don’t have that level of statistical, biological, chemical, or physical insights, money, personnel, etc. to solve these types of problems at that scale. So while most people’s experience with AI/ML is social media and office work, a lot of ours’ is not.

Just_Learned_This

Knicks ML, I'm not just shilling my bags this time

ddy_stop_plz

They literally have 3.6 billion daily active users, near half the world. Also one of the most normal P/E ratios of QQQ, let alone the Mag7 They have the most infrastructure outside of Google, Amazon, MSFT, and are moving like their life depends on it. Their new model is already borderline with GPT/Gemini, and they have the best ML algos in the world already.

c-u-in-da-ballpit

I’ll comment this again Cost of launch on Falcon 6 was $20,000/kg. Falcon 9 was $2,700/kg. That’s an 86% drop. Google’s research for their orbital data centers states that the orbital compute economics close at $200/kg. Starship is projected to be $94/kg. It had a successful test flight like two weeks ago. There’s already orbital satellites running AI/ML workloads for the ISS. A small LLM has already been trained on an in-orbit H100. Not about this is pie in the sky impractical Sci-Fi. The concept is literally in active Beta testing.

DJBFL

Jumping in here... He was asked if he or anybody in the organization had contact with them and discussed anything about restricting or preventing purchases. [https://www.youtube.com/watch?v=ML9vX2Pyumc](https://www.youtube.com/watch?v=ML9vX2Pyumc) Knowing what's in the messages, and in the screenshots in that video, and what actually happened, can you honestly say that YOU believe they had those conversations on the general situation and not paying them the full PFOF without ever touching on the option/likelihood/need/desire/effects/repercussions of setting it PCO?

packet

amzn, nvda, tsla, eth, btc, I got my lifetime supply of 10 baggers at the start of my investing journey 😭 the key is to have extremely high conviction and not sell. Unfortunately my nvda 10 bagger happened many years ago and I sold it all for what could have been a 500+ bagger. Bought them at 10 billion market cap because I liked their gpus and everyone I knew doing ML had zero intention of ever upgrading their macbooks when they switched to AMD. I have averaged 30% returns for almost 20 years so it's hard to complain but my 14k nvda investment would be over 7 million today if I just sat on my hands and ignored it. I also formatted a hdd with 10 btc on it that my IRC friend sent me when it was worth a fraction of a penny in 2008 and didn't buy it again for almost 10 years. RIP. I want to say the key is to come up with your own unique valuation and growth models and ignore what everyone else is saying. I'm a SWE specializing in distributed systems so I think I have good insight in to a lot of tech plays but the reality is the market has ripped so hard since I started investing just before the 2008 global financial crisis that it's pretty hard not to be a huge winner if you just kept shoveling in to the market.

KittyInspector3217

Huh? Are you drunk or somethin? “Its over priced”…”so are you arguing people will pay when its 20x more overpriced?” Theyre not paying today. Its propped up by regurgitative tokenmaxxing at enterprise companies on the dream of being able to fire all the humans and outsource to india. I work at one of those companies. In AI and ML systems. There is no viable consumer product.

c-u-in-da-ballpit

Same way it’s been done for 60 years. Radiating into space. I mean this isn’t something that’s just science fiction hoo-ha. There’s billions being poured into this rn. Starcloud already flew an H100 in orbit and trained an LLM on it. Their next satellite launches in October with 100x the power and the largest commercial deployable radiator ever flown. Axioms AxDCU-1 is running containerized cloud-native ML/AI workloads for the ISS. Arizona State researchers demonstrated that vanadium dioxide coatings shows a 6-fold enhancement in radiative thermal conductance. Sophia Space, backed by Nivida, is claiming 92% of generated energy to processing tasks significantly reducing heat waste from some novel GPU architecture. That prototype is set to launch in 2027. Google’s claims it’s new TPUs consume 15–30x less power per operation than GPUs, meaning 30x less heat per computation. Cerbreas claims the same for its new GPUs Idk what SpaceX is cooking up. But a company that can build reusable rocket ships likely has some pretty smart engineers. It’s just a complex engineering challenges with huge amount active funded research behind it - there is no thermodynamic wall. The payoff is huge. People will figure it out.

supershinythings

You’re missing HPE and Dell. HPE has invested heavily in SAAS for AI/ML, and it’s starting to pay off. Dell is also starting to take off for similar reasons. Storage and virtualization are both leveraged for AI/ML workloads. So Broadcom/VMware, structured and unstructured data storage solutions like Oracle, and others focused on low latency tiered storage and migration. One rips through terabytes of data to build a model; that model needs high end storage but those terabytes can go to tape or even get tossed when done. Being able to manage and prioritize data storage and access latency means only spending minimally for expensive, liberally for cheap, and moving it all around in between. Anyway AI/ML is currently the “killer app” driving tech. Everyone is looking to get products into those data centers. Manageability of data centers is also hard and key. AI observability lets you manage massive arrays with small human investment. But data collection and processing are continuous and ongoing. And of course, SECURITY. So much focus on something NO ONE seems to get right. It’s a continuous arms race, each side leveraging the latest tech to thwart the other. And investment is required because of the value of the data being invested in. Spending billions to develop an AI or ML model that is easily stolen by competitors and ransom jerks doesn’t make for profitable business. When you put all your eggs into an AI model basket, you simply MUST watch that basket.

xploeris

LLMs are hitting diminishing returns on training. The way forward will be linking LLMs to expert systems and other forms of ML that are more reliable than a super-autocomplete. Probably also see multi-agent schemes that can audit each other, collaborate on problem solving steps, etc.

TheLegendOfSteven

Figma is a deep value and growth stock. They are integrating AI tools into their editing software. I think some software stocks are way oversold, like Axon and ServiceNow. Some crappy ones like Hubspot, Asana, etc will be definitely losers if they don’t adapt quickly. As someone in the field, I don’t see ML getting good enough in the next couple of years to efficiently edit pictures to the level of trust that Figma commands.

aDonkeyWithACarrot

You should fullport ASTS like me https://www.reddit.com/r/wallstreetbets/s/YT47yt22ML

omgimdaddy

You understand the hyperscaler market is much larger than pc and is the primary driver of the current growth right? Literally just look at nvda prior to the lets run ML on gpu white papers.

saranagati

> vibe coding… Vibe coding is actually something where you don’t review the code but ignoring that, your concept of time to write vs review code is way off. I can write and review in a day that would easily take 2 weeks without LLMs. And that’s not even including having it write things in code im not familiar with like if I’m trying to make a UI. Yes it’s easy to have it write bad code but as people are learning to use it better, so is the code. I have many agents that do different specific types of reviews that the coding agent fixes before it even gets to people reviews. By that point there’s actually not many corrections I need to have it fix. > pre-existing tools Yes they existed but were often inaccurate for multiple reasons. LLMs can be too but for different reasons. Sites like Angi often weren’t accurate about a specific geographical region coupled with a specific service and product. You could figure out an accurate price by combing through many different sites, so it wasn’t something that couldn’t be done before. However LLMs have all that data and can look through many different sites much quicker than a person can. It can then aggregate that data and output it in a usable, often accurate format. Similarly, step by step guides did exist in the past. The things that have been great for me though is that sometimes I’m working with something that doesn’t have an easy to find step by step guide or I don’t know the specific name to search for. LLMs have been good at either identifying what exactly I need to search for, giving me general enough directions on how to use something. Something that probably would have taken me an hour in the past just to figure out what to look up. LLMs aren’t actually something completely new. They just put human language on top of complex ML problems that deal with massive sets of data. You should still do all your validation of what it says just like you should verify what you read on Wikipedia or anywhere on the internet. > railroads created more jobs So is AI. Who do you think are building these chips, the datacenters, the fiber optics, the generators, the LLM prompts, training the models, building the servers, creating the LLM based products? > commerce and flow of goods How long did that take for people to get? The railroads were expanded for the war. Once the war wasn’t going on any more, companies had to consolidate somewhere so that they could mass produce enough goods that they could be sold “exponentially.” They also had to get enough people to move to those locations to produce them. Until all of that happened, the average person didn’t see any benefit. The point being that didn’t happen immediately, it took decades (and a civil war). LLMs have existed for about 4 years so far. > please explain to me blah blah blah It’s not different than what AI is doing. The point of the entire post was to respond to the claim that railroads were great in every way and AI is horrible in every way. They both are good and bad.

samettinho

My team and I do ML, AI, MLOps work in a small startup. we love it.  Devops and fullstack teams also love it.  We have very small amount of tech debt and our development speed is quite good.  I havent used codex for quite some time, so cant tell if it is better or worse. I used opus 4.7 from cursor, it was amazing, then switched to claude code. As llm experience, I used pretty much all models, all models from gemini, 10s of chatgpt models, kiwi, composer etc. So, I have a good understanding of which models can do what.

GreenGameboyColor

!banbet PSG ML

gstxprz

Wouldn’t call myself lucky per se. I guess we all benefit from luck nowadays. But I have worked in the AI/ML space for the last 9 years and there was plenty of reasons to see NVDA as a good investment.

OilCanBoyd426

No way they actually have $500M revenue. They are a mobile marketing company, mostly a DSP doing acquisition and retargeting. Think very very small version of The Trade Desk. So revenue here is the amount of money they spend for brands the profit is the margin thier DSP collects from the media. Tough business and extremely competitive, Liftoff is not a leader in their vertical. Mobile marketing is past the boom days of 2010-2017. $500M is what they claim they get from brands to spend (they take a brands marketing budget and spend, no different than how an ad agency does) and there is just no way. I would be surprised if it was $100M a year. There is some serious number puffing here. They are a DSP. Overnight all their ML algorithms the DSP uses to make buying decisions became AI. Not an AI company. Also they’re not at all a leader in their space. Moloco is far and away #1 by a mile. Then a bunch of other companies Remerge, Dataseat, Jampp, Bidease with Liftoff firmly in this purgatory of mobile marketing companies that either got acquired for peanuts or missed the boat on mobile gaming hayday in 2015ish and didnt find a buyer and now are just stuck surving. If you buy this stock you are just a dumb person or a friend of the founders and trying to be nice. They’ll be delisted in a few years maybe sooner.

-0x00000000

I really like the photonics & quantum sectors going into the next 10 years. Volumetric photonic processors running analog ML models are far more efficient than GPU based solutions. MIT recently built a proof of concept using refractive index & angle of incidence to map models to volumetric substrates. POET $1000, QBTS $1000

zztopsthetop

The biggest reason for the improvement from 3 to 4 is the huge improvements in resolution and quality of sensor data & the bigger /faster inference chip they have in hw 4, allowing for bigger model architecture & 16 bit float instead of 8 bit float computation. They also went from 1.2 megapixels to 5 megapixels, which greatly improved data quality. You're talking about hardware innovations, those again have nothing to do with the ml engineers at Xai (because those aren't typicaly hardware engineers but ML people). Software innovations (and differences in training methodology) would be visible through version differences on identical hardware.

SoulCycle_

meta revenue growth is from traditional ML applications. The capex bet is for LLM training/applications

LackToesToddlerAnts

Yeah nobody asked you this lmao Love these engagement comments. In what world does “big time player” asked you to replace Windows outside of it being a joke? You are saying this guy knows the costs of operating systems and ignorantly wants an ML engineer to replace it? It’s a cute way to show the reliance of windows OS but Reddit eats this shit up so good job on your engagement

CuteSaggisture

Lol ai has been useful since before 2025. Image recognition being prime example for it. Lol so many ML algorithms being used in wallstreet since god knows when. I have no clue about the recombinant and transformationational creation would look into it.

asdf_lord

They absolutely can create, either recombinant creation or transformational. Both have been observed in academia particularly in mathematics. I have been using ai and ML since 2010 and while it may seem like we've got a logarithmic growth in latent abilities and performance we've not seen that in actual usage. AI is currently still in the early stages of adoption and has only been meaningfully useful since late 2025 for functions outside of chatbots. (Agents, research, and data pipelines).

ouhshuo

cortex AI is better than genie, I'll give you that. but for mature AI/ML, especially mlops either via model serving or containernisation, databricks is way ahead. also snowflake you need a bunch of supporting resources such as dbt right? in contrast, everything can be done in databricks

Gamplato

ML in DB is definitely a good experience. Since our data is Snowflake, we use their end to end ML and it’s super good…but this wasn’t always the case, I hear. The Databricks applied AI is not close to Snowflake. We tried their coding agent recently just to stay up on things. It was actually shocking how immature it felt in comparison to Snowflake.

ShanghaiBebop

Databricks and snowflake is very much converging on the same core things.  Databricks has better AI and classic ML workflows integrated with their platform. snowflake has a better first class warehouse experience OOTB.  Bigquery imo is pretty far behind both of them.  Both DB and SNOW will see significant rev growth acceleration from AI though, since agents will need to query real enterprise data to keep themselves grounded in reality, and is drastically dropping the barrier to querying data. 

Obvious_Inspector779

I mean if they’re good analytics for people who pay for ads sure…I cannot believe this is the major play with the new capex though…I still think they’re cooking something else or maybe just organically increasing the infra for their already ongoing ML/data stuff.

sevopapi

Now add on the large amount of engineer/data/ML job postings for Reddit, which won’t be cheap. This story is just starting.

ActivatingEMP

Yeah I agree on ML but that's not what is getting all the investment, it's these stupid LLMs.

StegersaurusMark

I suggest you look up the statistical results of “active managed funds”. Your ML acct manager was probably blowing smoke to justify his paycheck. I mean he might have believed it, but it was probably still smoke. Then you went and parroted it to your next manager. I can’t offer you any advice. I manage my own accounts. I’m 40. I’ve interviewed a few FA/ account managers and always felt skeeved out by them and walked away. I’m now “using” the Fidelity wealth management but I don’t think I’m getting much more than the basic Monte Carlo sim you can access from any old 401k account these days I’ve been devouring the Rational Reminder podcast over the past year or so. It’s Canadian but still really great IMO as an American. I just tried listening to some Motley Fool podcasts and just don’t feel it.

richierva

I wasn’t doing this ML was.

richierva

ML did. It’s a pretty common strategy.

richierva

I understand the performance of the bonds I don't understand why the underperforming ETFs from the sector rotation plan haven't been addressed. We had a more conservative approach with ML and now this FA doesn't do anything.

BertRenolds

Maybe to being a loser? But I work in software for a living, and reading this post, it's pretty clear they are just using AI slop and calling it ML.

Weak_Emu3659

Is it okay to buy a $25M house? Our household liquid net worth is \~$20M, mostly in S&P 500 holdings with about $5M in unrealized gains. Our annual income averages $10M pre-tax but is highly volatile—ranging from $5M to $25M over the last few years. While we expect this to continue, we’re aware that in tech/ML, things can change quickly.

iHubble

Brain dead take. Meta stole nothing, they just massively improved it and the project got naturally subsumed with scale and resources, as has happened with countlessly many smaller open source projects. If anything, PyTorch is probably the greatest gift to the ML research community of the last 10 years.

Ill_Sun358

Spurs on the ML big homie

SoulCycle_

what about the ML framework used by almost every single MLE in the industry?

pwdbypenguins

Watson was ML

_101010_

So I was talking with a couple people at anthropic. And I guess their belief is that it won’t converge. Basically whoever can get the ML to iteratively improve on itself first takes the bag

pm_me_your_pay_slips

To be clear, the anecdote above includes frontend, product and ML engineers.

-UndeadBulwark

I'm not sure about Intel but AMD is doing fine. AMD has diversified so aggressively over the past decade that framing them as a distant second to Nvidia misunderstands what the company actually is now. They're in consoles, desktops, laptops, tablets, phones via Samsung's Exynos licensing, servers, and routers. Ten years ago they were close to folding. That turnaround is not a small thing. On the software side, ROCm has closed the gap with CUDA faster than almost anyone expected. A year ago it was genuinely painful to work with. Now with ROCm 7.x, Windows support has arrived, PyTorch and most major ML frameworks treat it as a first-class option, and the 7.1.1 release delivered up to 5x performance gains over 6.4.4 across key AI models. It's not at full CUDA parity yet in every workload, but it's no longer a footnote. UDNA, which merges the RDNA and CDNA lines into a single unified architecture, is where things get genuinely interesting, and that's still ahead of us. On Nvidia's position in AI more broadly: this cycle has a pattern. One company moves in early, captures the market, prices rise, and the market diversifies to reduce the dependency. The current shift toward local inference first, cloud escalation second, with models like Gemma 4 running on-device and Google pushing AI into phones at the hardware level, represents exactly that kind of structural change. Nvidia's dominance is built on centralized cloud compute demand. If the architecture of deployment moves away from that, the moat shrinks. AMD has been methodical and capital-conservative. Nvidia has been running at full throttle on the assumption that the demand curve only goes one way. That kind of overextension is exactly the setup for a Zen 2 moment.

dintclempsey

...Huh? Where was I disrespectful? Jesus. Cloud is one of the fastest areas of growth for Google atm, driven by... enterprise AI and ML offerings. This isn't about your banker friends using Gemini. (Although now the largest operating system in the history of the world comes with it embedded everywhere.)

Glum-Ostrich-4250

The llm models themselves aren’t fully reliable tho. From what I’ve seen is the ai boom inside the tech industry has led a lot of companies to develop different types of ai for different things like ML algorithms for fraud detection. It’s not just slapping an LLM to something and calling it a day

demaraje

Get off your high horse with this patronizing shit. I'm a ML Engineer. I know how this works. I use it and build it on a daily basis. You're literally talking about SDD like it's some godsend. It fucks up A LOT. Talking about Opus 4.6 Yes, good prompting and planning makes a huge difference. It still fucks up. Do a simple experiment. Write 10 changes by hand and save the git patch. Then write a simple prompt/agentic flow/ReAct flow to produce them from a simple description. One flow for all. Calculate the semantic difference to what you wrote. Then you'll have an idea of how well it performs.

drankleanonce

take your money and go all in on cavs ML tonight your welcome

Master_Enthusiasm410

The bear case about big tech building custom silicon (Google TPUs, Amazon Trainium, Microsoft Maia) is real but consistently underestimated how long CUDA lock-in buys NVDA time. Every ML engineer in the world has years of CUDA code they'd have to rewrite. That's not a moat, that's a fortress. The China H200 approval is the wildcard - if Chinese firms get access, NVDA's revenue ceiling just moved up significantly. Jensen on Air Force One is the most Silicon Valley sentence ever written.

Flat_Editor_2737

Well said. The circular deal narrative is easy for the media to present, and rational enough to believable. It's not wholly absurd to consider as a risk but the performance obligations and 'seeding the market' piece aren't new. Hyperscalers have been doing this for awhile. Ive been in tech infrastructure sales for 14+ years working for companies you would absolutely recognize. Ive sold everything from data center hardware, to cloud infra, to big data saas, to ML, to AI, to Agents. It impresses me how confident the narrative is that AI ROI is playing pin the tail on the donkey in the dark. This might have been true in 2023. I'm now on the professional service consulting and integration side now. In the last 6-12 months the models I'm seeing companies walk in with for use case analysis and the way they are quantifying TCO/ROI is getting orders of magnitude better on a month over month basis. Companies operate on a spectrum of competence, but the financial models at the ones who will likely be successful are iterative already. That knowledge will eventually be more ubiquitous and you'll see the same thing you saw with hyperscale infra - a smaller first mover demographic followed by a larger adoption wave as the risk starts to normalize. . We are still early, IMO.

SkyFew229

iren is ran by retards, NBIS is run by one of the strongest AI/ML teams ever

tristis_veritas

$2.52T in total 2026 * AI Infrastructure: $1.37 trillion * AI Services: $589 billion * AI Software: $452 billion * AI Cybersecurity: $51 billion * Data Science / ML Platforms: $31 billion * AI Models: $26 billion https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026

tristis_veritas

$2.52T in total 2026 * AI Infrastructure: $1.37 trillion * AI Services: $589 billion * AI Software: $452 billion * AI Cybersecurity: $51 billion * Data Science / ML Platforms: $31 billion * AI Models: $26 billion

Chumphy

Oh yeah totally, I was just curious. I was asking because not only do they have sensors and cameras that go into ML tech, It’s also a company that is used for the opposite of AI things. Actual movies, cameras for live events, human tech so to speak. They are both complimentary and opposite of AI stuff. 

skilliard7

Software engineer as well, background in AI/ML. I bought a ton of Samsung/SK Hynix back in late 2024 because I knew HBM would become the bottleneck due to growing model sizes, growing KV cache with context size. I foresaw companies producing their own AI accelerator chips, and figured HBM was the common bottleneck, not Nvidia. I also had some WDC(pre sandisk split) because I figured data was important to AI training. AI coding tools definitely improved a lot recently to the point where they can no longer be ignored. And this obviously explains the recent compute shortages; AI coding usually requires intense reasoning models, and millions of engineers suddenly using it is going to cause a surge in demand. But I think inference demand growth is going to slow down from here, though. Most large orgs are already pushing devs to write most code with AI, and all employees in general to use AI. Therefore, there needs to be another massive application that LLMs take over in the very near future, or we will end up with substantial oversupply of compute as new datacenters come online. I think what's very telling is that all it took was a 300 MW SpaceX datacenter for all of Anthropic's compute shortages to be magically solved, and SpaceX was able to give it up without impacting Grok's availability. Additionally, OpenAI shows no signs of shortages, offering extremely generous limits on $20 plans. A 300 MW datacenter solves the compute shortage of a leading frontier lab, and yet there are several Gigawatts of capacity planned each year, and compute also gets substantially more efficient with each hardware generation. This suggests that the "compute shortage" is quite marginal and oversupply is nearly imminent.

Intelligent-Love5146

Similar. I’m in Data Science/Data Engineering/ML and also followed a similar pattern - using ChatGPT to debug to using Claude locally to write code and merge PRs out of whole cloth. I’m not wholly convinced on your thesis. Models are becoming more efficient. It’s not clear whether token usage is simply going to increase exponentially. My opinion is that SaaS and Software stocks in general are doomed. Sales Force, Workday, DataDog etc It’s clear you can build highly customized software for basically a few dollars worth of tokens. You then have to figure out deployment and change management but the value add of cookie cutter SaaS is way way lower than it used to be. Agents can do so much now that you might not even need software UI at all for stuff that used to require standalone apps.

Pale-Entertainer-386

Andrew Ng believes that we are still technologically far from achieving AGI. He worries that companies investing in AI today could easily face disappointment, potentially leading to a sharp decline in AI investment and the bursting of the AI bubble. Andrew Ng is one of the most globally recognized leaders, pioneers, and educators in Artificial Intelligence (AI) and Machine Learning (ML). [https://youtu.be/4vzmTKUFtxg?si=g6eTFcjRlpevGHW_](https://youtu.be/4vzmTKUFtxg?si=g6eTFcjRlpevGHW_)

Jagged_Tide

What I found in a ten second google search on (ironically) a Reddit thread: “A lot of python that’s used for ML like using tensorflow or PyTorch is like an API for the underlying c++ code. It’s when you have to really optimize things that’s when you would dive into lower level” I don’t know much about C++, my understanding was it was used primarily for like games and web design but yeah looks like there’s some practical usage for it in ML. That being said, I feel like a highly competent Python coder would almost never need to dive to that level. At least from what I’ve heard they don’t.

Cold_Specialist_3656

ML is everywhere. Massively expensive LLM inference of huge models is not

Cold_Specialist_3656

Basically nothing runs on AI so far. At least nothing that would justify such valuations.  Most ML use is still for vector embeddings, scoring, sentiment analysis. Tech that's been around for ages and takes 1000X less compute than LLM

jbokwxguy

AI in modern terms generally means LLMs and Generative AI (image creation). Not the ML AI that is used in Medical

ScarletHarpy93

What do you mean it's not a sustainable business model and inevitably people will realize it's not price effective to replace workers with ML?

Plastic_Ostrich897

Not too late to but money on Detroit ML @ the half +870

aedes

This all depends on the balance of how long it takes these companies to start making profits from their AI expenses, vs how long investors have the free capital and hope for the future to keep giving them their money.  No one is making profit from selling AI right now. If rates remains low and the fed continues the QE they started a few months ago, and these companies find a way to either significantly reduce training and compute costs, or convince people/companies that something like Claude or better is worth 5-6 figures per employee per year, then we will avoid problems.  If recent geopolitical problems aren’t resolved and prices of power and electronics manufacturing continue to rapidly increase, if ML researchers have truly reached a plateau in what transformers can do, if money becomes less available due to the need to increase rates or some sort of liquidity crisis, or investors just lose faith that profits are coming or get spooked, we will be in trouble.  In either case, the longer it takes for these companies to start turning a profit, the higher the risk of a correction. 

Leveraged_Lots

"I can’t hire AI/ML people fast enough." As a shareholder, if I heard this on an earnings call, that would mean selling immediately. AI might prove amazing, but you won't be spending my money betting on it.

raisedeyebrow4891

That’s too bad you feel this way about AI. I work for a Fortune 500 company. One of the best pharma firms in the world and I can tell you that our AI modernization efforts are in every single facet of our technology, research, manufacturing, and R&D. I can’t hire AI/ML people fast enough. The main SaaS providers are all building out agentic orchestration layers. The business demands it and if we let up the competition is going to eat us alive. I use AI for my own work every day and it has made me infinitely more efficient and able to produce at a level I cannot dream of. So I would be wary of judging the entire industry by your own anecdotal experience. Just go on LinkedIn and search for AI linked roles across all industries and tell me it’s a passing fad

Apocalyptic-Post

How do I parlay 0DTEs and the Timberwolves ML?

CuriousAndGolden

I worked with a programmer who was really smart and built a killer day trading app. He was always broke and wanted his paycheck early. I think it’s a combination of chaos, insider trading, and an arms race between whatever ML or AI apps there are out there. It’s like betting on horses. You need to somehow know more than everyone else to consistently win, and that’s really hard to do unless you’re cheating.

C130J_Darkstar

OKLO close to design approval, use this info as you wish. https://www.nrc.gov/docs/ML2608/ML26086A285.pdf

NeedMoarLurk

I work at a biotech and we use a number of "AI" (ML) based tools. They're pretty cool and have come a very long way in the past 2 years. Not sure how they can get you from Ph 1 to Ph2 quicker though, as those stages and progression between them require (positive) clinical data, not AI predictions..

ScarletHarpy93

I think they are going to be built, and then they'll end up like dark fiber from 2000 - 2009: noone can afford to use them because the market will see them as commodities and the ML business case will fail to fill out the demand.

InitiativeGlum2507

I lean long, and I use Schwab. One platform for investing. Another for banking. I set up two brokerage accounts. One is cold storage. I have a little money in ML but that's just to qualify for some benefits. Pro-tip: I gamble like an absolute r\*tard in my IRA and have been wildly successful, without having to deal with tax implications every year.

Ok_Freedom3290

Solid breakdown. It’s interesting that you found RSI more effective than premium size—definitely a lesson in not chasing overcrowded trades. I’ve been experimenting with a similar approach but I focus more on real-time whale movements and 'Z-scores' of the order book. For example, **I'm seeing a Z-score of +1.14 on RIOT right now with a massive relative alpha of 6.79%**. Tracking those huge blocks *before* they hit the tape is where I've found the real edge. I actually ended up building a live terminal for this (ML engine and all) just for my own trading so I could dodge those high-premium traps. If you want to see the data I'm looking at, it's live here: [https://alphasignal.digital](https://alphasignal.digital/)

kinetic_honda

I'm talking about things like evolutionary scale model from Meta which is a protein sequence language model that helps with predicting experimental outcomes using purely sequence alone. Imagine that??? Can you imagine certain efficiency boosts that can be achieved using such tools?? Want to talk about other such machine learning workflows and solutions these tech companies are developing? Or do you want to simplify it to chat bots because that is what you have used? I'm glad to chat further. Have you used other ML models such as phikon for ihc slide scan based predictive models? Something that you can use to train classifiers to identify cell types from scanned tissue images?

AnonymousUser462

Other ML algorithms and especially attention have solved text translation easily since 2013. Voice translation is a bit more of a new game changer, but people on this app are largely there to learn English or have a hobby, not necessarily become fluent to speak on a day to day.

TonguePunchMyPoopBox

Ding ding ding. Nvidia consistently submits across all of MLPerf for several years. Competitors only submit in certain benchmarks when it favors them (not so often). Submitting to MLPerf requires a lot of effort, too. The kind of effort requried to setuo and optimize large scale rujs on major datacenters/clusters. Top answer in this thread talks only about matrix math as though that’s the only thing large scale ML/LLMs are doing. It’s a full stack optimization problem from storage to memory to networking to compute to software orchestration etc. Jensen’s been talking about this for years.

SuffiecientSapian

Man crazy ass games yesterday! Had Raptors and Lakers ML

MaizeandBlue94

I graduated from b-school in 1994 and was offered an interview with Merrill Lynch's treasury office. I flew to NYC and the woman who interviewed me wore the most revealing shirt that she also had strategically unbuttoned just so. I struggled to maintain eye contact and spent much of the interview trying to avoid staring at her breasts.I knew immediately I wouldn't get an offer. It all worked out though as ML treasury was located in the WTC and we all know what happened a few years later.

jhplano

Hi PaPa, First off, I'm a former Nasdaq market maker, held series 7,24,55,63.....now I'd be considered a retail participant. I can answer all your questions: I entered all these trades thru the 'spread' order entry ticket page. I have the highest level of options approval. Yes, this is a Reg T margin account, ML EDGE doesn't offer port margin. So none of those are not true. Yes, some brokers are more conservative and have stricter house rules. Yesterday, the agent said this is a FINRA Rule 4210 deal, not elevated house rules, which he's probably wrong. I've got other accounts at ML that are mid-seven figures but they are not 'tied' to this account. I found out that ML just isn't a good platform for active trading, either equities or options. This is where I whine a little, I had a huge back surgery last Nov, it worked out but I have a new issue that will require a revision procedure. My patience is short, but I feel a little better this week, so I thought I'd get in the pool, stay in the shallow end. This really is making my upset, confused. I elevated the issue up, they'll probably get back to me Monday or Tuesday. I do have access to Brian Moynahan, the CEO, but I hesitate to get him involved, that'd be silly. Thank for getting back to me. After a quick look at some brokers last night, I like TastyTrade and WeBull. I simply need a broker dealer that caters to active trading.

SuffiecientSapian

I had 76ers ML last night but big money on Denver. Man wtf happened to that Nuggets team. You have a multi time mvp, champs 3 years ago and now can’t even make it out the first round against a team missing 3 starters. Fucking embarrassing

RepulsiveDisaster671

#Hope yall took the knicks ML

SuffiecientSapian

Hawks +10000 ML I’m taking that