ML

Busy-Soft-6209

Don't forget that AI, or A1, as Trump's puppet who is responsible for the education system in the US (triple facepalm) says, has been around for quite some time and is already implemented in many products/services, there is a lot of predictons (AGI) and it is pretty difficult to see into the future, at least for me, but I am an engineer and work in this field (ML) so I know a thing or two and imo AI is much different from the internet and will never be as revolutionary as the internet itself, as it's (the internet) being used to connect the whole world. On the other hand, there's definitely a huge potential and AI can help millions or even billions at some point and will play a huge role in the future. But if you have a manual job, I'd not be too afraid if I were you

Vivid-Scallion-4031

Going jays ML to make up for puts losses this week, GS

rackoblack

[https://www.google.com/search?q=compare+merrill+lynch+and+charles+schwab](https://www.google.com/search?q=compare+merrill+lynch+and+charles+schwab) Read through the differences. Your post describes someone who wants Schwab, not ML.

Great_Northern_Beans

I also left out Chevron, Lodge Cooking, and Toys R Us. My comment wasn't intended to be an enumeration of every ML team in existence, just a few examples as a frame of reference for how developed Apple is.

Great_Northern_Beans

While I agree that they haven't done anything innovative in quite some time, what does "behind on AI" even mean?  They don't dump hundreds of millions of dollars memeing on LLMs with little discernible value to their product, but their ML teams are absolute fire. You should see what the interview process to join them is like - easily more difficult than Amazon/Meta, and on par with Netflix/Google.

ElectricalGene6146

I’m so tired of that argument. ML frameworks have caught up, hence why everyone is buying AMD now.

greyenlightenment

should have taken that ML /LLM job 3 years ago ...

gatorling

Yeah, it's true that LLMs seem to be really good generalizable information retrieval models. Good at encoding complex relationships between words and regurgitating desired data (with hallucinations) back at the user.  That's why the market is spooked about Google I think. LLMs seem to be a way to erase the tech most that Google has built over the last twenty years.  Now is ChatGPT better than Gemini in objective measures? Well.. the best measure we have (I think) is the blinded tests from lmarena, which show that Gemini pro 2.5 has been beating all other models in nearly every category for months now.  So Google isn't behind when it comes to the product.  Additionally Google is able to serve Gemini at a fraction of the cost that OpenAI can. After all Google has been building data centers from the ground up to reduce the cost of their services for over twenty years now..and they've been tackling the cost of ML inference and training for about  8? Years.  Google designs their own silicon, their own cooling, their own board layouts, servers and data centers to squeeze every ounce of efficiency at the physical level. They wrote their own compilers, write their own software... Everything is vertically integrated, from the silicon up to the webUI.  ...and that matters a lot, just look at OpenAI. They just can't run on Azure, it's not cost efficient enough.. they have to start building their own data centers to achieve efficiency at scale.. and getting an entire data center up and running in 2-3 years is a gargantuan feat.  So OpenAI had a 1 year head start on Google, and in 2 years Google has caught up/surpassed technically.  If I had to bet on one horse to win the AI race, it'll be Google. But it's entirely possible that winning the AI race will still erode search profitability... Traditional search is going to be cheaper than AI search for a while longer... And that means lower profit margins. So yeah, you can still win the AI race and still manage to lose. 

greenpride32

Migration of k8s and onboarding across multi-clouds is completely different projects. Migration would account for a very large window of testing and validation. My CV history includes 2 megacaps larger than GOOGL so I'm well aware of how to scale, and also what the boundaries of ML engineer's responsibility would be. I don't really see the point you're trying to make - 10+ years of NVDA GPU/CUDA dominance - if anyone gets it, it should be you. I mean are you just being bias because of your GOOGL equity?

Good-Way529

ML engineering involves a lot of infra work. Not at google but at large SAAS companies most of my job is related to data scale rather than algorithm design, feature engineering or tuning hpps. Your 3rd paragraph could not be more wrong. Switching production workloads between cloud providers is very very difficult, even if most your compute runs on k8s. It took a team of 50 about 2 years to migrate GCP->AWS. Compared to that a migration from TPU to GPU is actually a lot easier since it’s handled at the library level. It’s still very hard since hardware has to be tuned and code has to be refactored, I’m not trying to undersell it but a company like OAI which burns most of money on cloud compute and engineering salaries would be highly incentivized to support both as early as possible

greenpride32

I didn't say "software lock" was the only reason, if you want to term it that way. As ML engineer you most likely aren't involved in infra and hardware architecture, you are just the consumer of it. It's just a "tool" provided to you. If I am a large SaaS software provider, I can deploy to AMZN AWS, MSFT Azure, GOOLG GCP or ORCL OCI using Kubernetes and dealing with small naunce of different clould provider API's. Not ideal, but achievable. What's you're talking about is nearly complete rewrites of code. Substantially less than ideal.

BB-68

Maybe we will, but AI today isn't much better than the ML "revolution" in 2013. That was supposed to predict behavior, improve decision making, and take humans out of the loop on all kinds of decisions. Tons of companies IPOd in that era under the guise of machine learning and predictive analytics. Almost all of them are worthless just a decade later because the entirety of their business model was a house of cards. Companies like Warby Parker, Opendoor, and Grubhub were supposed to reinvent their industries using machine learning and predictive analytics. All these AI startups that are going to replace entire divisions of companies are peddling the same BS as their predecessors. It's just a slightly different PowerPoint deck

mayorolivia

It’s good as a short term investor but long run Apple is missing out on the data and ML benefits. OpenAI and Google will use their data to create better mobile operating systems than Apple.

Gloomy_MTTime420

So when you’re wrong you think a good solution is to double down. Then after doubling down you are shown, once again, how incredible foolish you look…you resort to calling other people fools. Seems like a good life skill you have there. Funny how seem to have forgotten that for a decade before AI, they just called it ML and it had no clue what it was doing, the tech wasn’t there (thanks $NVDA for that!), and it was just old boring machine learning that didn’t offer anything more did anyone mention it because it actually devalued a company. Cool story. Nothing gambling about it. But it has been 10x picks.

CavulusDeCavulei

Will not happen if they don't have CUDA or something compatible. Every library of machine learning is compatibile with CUDA. That means that I can take any ML algorithm and it will work, and that's invaluable

ElectricalGene6146

Nobody said it was easy, but it’s clearly not a moat either. Nvidia is priced like this because the market expects them to maintain ludicrous margins when in reality software for ML frameworks is quickly catching up hence why plenty of AI applications are migrating over to both AMD and ASICs. Nvidia price/ performance is absolutely terrible.

OcclusalEmbrasure

Obviously, you don’t understand the technology. But please prove me wrong. What is Palantir’s core technology that powers ML capabilities?

drewbaseball10

Respect to OKLO bulls. It definitely takes guts to invest in these reactor deisgners. **HOWEVER**, these are fundamentally different plays: OKLO: Microreactors targeting DoD/military applications (forward bases, naval propulsion, remote operations). Still pre-NRC application submission, 3-5+ year timeline. Unfortunately, they keep getting their applications flat out rejected. See 7 months ago (Atomic Alchemy-OKLO subsidiary since Feb 2025): https://www.nrc.gov/docs/ML2431/ML24311A058.pdf NuScale: 77MW SMRs are designed for hyperscaler partnerships and commercial baseload power. Only NRC-certified SMR, modules in production, ready for 2026+ deployment. My intuition: Retail seems to be chasing (and Wall Street appears to be nudging) OKLO’s “next-gen” narrative while missing NuScale’s immediate commercial opportunity. They have different customer bases entirely - Apple (or other AI hyperscalers) need(s) scalable baseload power for data centers, not microreactors for military bases. If you believe in July-August hyperscaler catalyst timing, only NuScale can deliver validated power purchase agreements on that scale. OKLO could *potentially* be the better 5-10 year speculative play, but NuScale sits atop the hyperscaler market right now.

ObservationalHumor

Hard to reason with these people because they want to believe so badly that everything Musk has told them in 100% true despite his track record of lying about damn near everything for the last decade. None of them actually know what HD maps are used for but they're convinced it somehow makes the system impossible to scale or that there's absolutely no other logic in there to deal with deviations in the expectations that the system has from its data and what it sensors are reading. Or that maybe having those expectations to begin with to realize something has changed or is unexpected is in itself quite valuable. They're also unaware that every system out there uses fairly detailed and highly annotated maps because... they're cheap, widely available and necessary to actually plan any trip that goes beyond the vehicle's immediate sensor range. They've internalized specious arguments Musk made a decade ago that were premised on the cost of LIDAR at the time and low volume HD map data for institutions to misrepresent the actual cost of a roll out in a new area. Of course none of them have actually done any analysis of what the existing rideshare business actually looks like and how most of the profitability comes from a few major metro areas. Turns out that from a financial perspective it doesn't really matter if your vehicle can't operate on a poorly outlined dirt road in rural Iowa because there's no value in deploying a robotaxi service out there to begin with. Large transportation hubs like airports and train stations also tend to be the single most important destination for any ride sharing service too. Something like the aggressive and blantly misanthropic traffic at LAX is going to be one of the primary proving grounds for any robotaxi service imho. High quality sensor data also apparently doesn't matter. I mean it's not as if these systems are complex probabilistic models at their core and there's a massive amount of effort that goes into limiting that through having reliable sensor data or trying to account for it via various estimators and potential scenarios to account for things that are a result of partial information and limited observability of the environment and other actors in it. Screw trying to interpret pedestrian and other VRU intentions, apparently the real prize is dealing with a roadway that isn't adequately marked. Let's also ignore all the prior statements from Tesla that actively conflict with the 'vision' Musk is pitching now. Their previous CFO Zach noted that FSD would never be a big value add in the longer term because it's a competitive industry and even if Tesla was in the lead it would only last for a few years. Also Waymo's sensor suite cost is apparently a huge issue despite Musk stating that once they cracked autonomy they wouldn't sell vehicles for less than a few hundred grand due to the massive value he claimed it was unlocking. But yeah $50k in extra sensors and being at least several years ahead apparently isn't really worth anything for Waymo. Not to mention the generally terrible record of prognosticating that Musk has personally had with FSD being a year away for close to a decade even when their vehicles fundamentally lacked the basic ability to navigate and act independently and were training on still images. In any sane world he would have been prosecuted for securities fraud (yet again) years ago. I think the truth of the matter is that Musk's decisions boxed development of both FSD and their robotaxi products ages ago. Fixing the sensor suite and processing hardware was a massive mistake and a completely backwards way to go about solving an open problem like autonomous driving. Virtually every other company properly identified that solving the problem was the top priority and optimizing the sensor suite and processing hardware for cost was something that would afterwards as both the problem and solution were understood much better. But that approach wasn't going to help Tesla sell cars back in 2017 and so Musk has fought vehemently against and spread a variety of misinformation about HD maps, LIDAR, RADAR, sensor fusion and just generally abused technical terminology and made nonsense statements anything related to AI and ML for years at this point. I honestly have no idea how someone can simultaneously be so wrong for so long and still be treated as some kind of credible expert on whatever topic he weighs in on (pretty much everything). You'd think everyone would be sick of him sucking up capital and funding and limiting the possible avenues for developing technology in a ton of different fields but apparently he actually had to do a few Nazi salutes on stage at a political rally before anyone took notice and even then he's continued to raise billions of dollars for his failing social network and late to game AI play.

Constant_Lack3821

ML/Software Engineer here! Here is what I like to say to people when they say AI is not a big deal or that it will take jobs. AI is like where the internet was in the late 90's. We are still early in the technology, and have soooo much space for innovation within the field.

ep193

$35k is enough to have a managed brokerage account. Would suggest setting up a managed portfolio at one of the major brokerage houses (Schwab, ML, JPM).

RaechelMaelstrom

I'm not ignoring AI, I just think it's at the peak of its hype cycle. I was programming ML in tech for years before ChatGPT, and that's all giant matrix multipliers. I agree, lots of data centers being built, and the cheapest and most undervalued play is GOOGL, which is also building lots of data centers for Google Cloud, which, having used it, is amazing, and I think much more put together than AWS. I'm also a long term investor of NVDA but I still think they're limited by how many chips they can crunch out. You're talking about politics, and crap bears say, and yet I'm talking in facts. But of course, I can always be wrong, or right, and the market can be right or wrong for as long as it wants to be.

Plastic-Edge-1654

This was the prompt. Must be using deep research. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING 1. ⁠⁠Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations 2. ⁠⁠Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds 3. ⁠⁠AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings 4. ⁠⁠Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals 5. ⁠⁠Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity 6. ⁠⁠Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) 7. ⁠⁠Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring

Plastic-Edge-1654

This was the prompt I wrote. Must be using deep research. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING 1. ⁠⁠Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations 2. ⁠⁠Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds 3. ⁠⁠AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings 4. ⁠⁠Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals 5. ⁠⁠Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity 6. ⁠⁠Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) 7. ⁠⁠Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring

Plastic-Edge-1654

This was the prompt. Must be using deep research. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING 1. ⁠⁠Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations 2. ⁠⁠Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds 3. ⁠⁠AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings 4. ⁠⁠Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals 5. ⁠⁠Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity 6. ⁠⁠Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) 7. ⁠⁠Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring

RamseyRumples

Here is what I entered based off of a subthread above. An I do it right, OP? I'm going to use Robinhood as my brokerage platform and start with $500. I want to hit a minimum of 50% ROI per trade, and the end goal is to generate over $1,000,000 with your trading advice and research, and I am medium risky, but I want to ensure the goal is hit. Here's the prompt. After this research is done, generate a prompt aimed at achieving my results with the highest degree of accuracy possible. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring - - - chat GpT wrote - - - Thanks for the detailed blueprint—this is a great foundation. To tailor research that gives you the highest possible chance of turning $500 into over $1,000,000 via options trading with a minimum 50% ROI per trade, could you please clarify the following: 1. Time Horizon: Over what period do you want to achieve the $1,000,000 goal (e.g., 1 year, 5 years)? 2. Trade Frequency: How often do you plan to trade (daily, weekly, opportunistically)? 3. Platform Constraints: Since you’re using Robinhood, are you okay with its limitations (e.g., no access to certain option spreads like iron condors)? 4. Bot Development: Are you building the quant bot yourself, or do you want research into third-party tools or platforms? 5. Technical Resources: Do you have programming capabilities or prefer a no-code/low-code solution? 6. Compliance & Automation: Do you want help navigating SEC compliance and Robinhood’s automation limitations? Once I have these details, I’ll initiate targeted research to help you hit your milestone with the best available strategy, tech stack, and trade ideas. - - - I replied - - - 1. Let's aim for between one to three years. 2. opportunistically 3. Yes. 4. I want you to research and build it. 5. No-code solution. 6. No.

Plastic-Edge-1654

This was the prompt. Must be using deep research. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING 1. ⁠Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations 2. ⁠Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds 3. ⁠AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings 4. ⁠Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals 5. ⁠Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity 6. ⁠Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) 7. ⁠Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring

Plastic-Edge-1654

This was the prompt. Must be using deep research. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING 1. ⁠Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations 2. ⁠Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds 3. ⁠AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings 4. ⁠Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals 5. ⁠Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity 6. ⁠Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) 7. ⁠Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring

Plastic-Edge-1654

This was the prompt. Must be using deep research. CORE STRATEGY BLUEPRINT: QUANT BOT FOR OPTIONS TRADING 1. Define the Trading Universe • Focus: S&P 500, high-volume tech stocks, and sector ETFs • Options: High open interest, tight spreads, weekly/monthly expirations 2. Data Pipeline Setup • Real-Time & Historical Market Data: Prices, implied volatility, Greeks • Economic Indicators: Fed statements, unemployment, CPI, interest rate forecasts • Political Signals: Election cycles, sanctions, fiscal policy shifts • Weather/Natural Disaster Alerts: NOAA, global weather APIs, satellite feeds 3. AI Signal Generation Modules • Natural Language Processing (NLP): • Scrape financial news, SEC filings, Reddit, X/Twitter, Fed transcripts • Use sentiment analysis + event detection to score macro/micro triggers • Time Series Forecasting: • Use LSTM, Transformer models, or Prophet to predict stock volatility and price direction • Event-Based Triggers: • Geo-political tension spikes, hurricane paths near oil fields, unexpected earnings 4. Quant Models to Deploy • Delta-neutral strategies: Iron condors, butterflies • Volatility-based: Straddles/strangles pre-event, IV crush post-event • Directional: Covered calls, puts, debit spreads using ML-classified trend signals 5. Backtest Engine • Simulate trade strategies over 5–10 years • Adjust for: • Slippage • Commissions • Margin use • Volatility skew • Liquidity 6. Execution Layer • Broker API: Interactive Brokers or Tradier • Build custom scripts to auto-place trades when your bot’s edge is confirmed (> X signal score) 7. Risk Management System • Max drawdown control • Position sizing based on Kelly Criterion • Volatility-adjusted leverage • Daily VaR limits • Portfolio correlation monitoring

Tactical45

I don't think you even mentioned the saddest part of it all. Mira is not even a seasoned AI tech leader - just happened to be in the right place at the right time. Before OpenAI was just a product manager at Tesla and had zero ML/AI/Computer science experience or eduction.

onyxengine

Its a specialized gpu unit specifically developed for machine learning by google. Its not the kind of thing i think of as something that makes gpus obsolete, and its also not the kind of thing Nvidia isn’t positioned to start making and quickly start making better if it became a concern. The code infrastructure for deploying ML heavily leans towards Nvidias hardware, they 90% of the market share in data center. I’m thinking more like quantum computing consumer access breakthrough that can train at speeds that make the fastest GPU like devices seem slow.

Be-ur-best-self

At the time I was working for ML and I found it at 9 thru research, bot it for a trade and sold at 14, shower when I saw it at 20, I put all my clients in and then jumped in.

theperezident94

The ML recession probability model on your source website is, uh, concerning.

QC_knight1824

Bayern Munich is getting + money ML over Benfica today your welcome

ufbam

Actually I asked ChatGPT who knew how to manufacture actuators at scale but also had a large machine learning cluster and all it could come up with was Tesla. Other honourable mentions were: Boston Dynamics (excellent actuators, less public on ML compute scale). NVIDIA (huge ML clusters, but not an actuator manufacturer). ABB or Siemens (industrial actuators, some ML research, but not at Tesla’s AI scale).

SteaknSalt

I put my projection based on my ML model

OptionSellingRegard

“alright, lots of money came into Pacers ML, so Halliburton we need you to fall and get injured so we fuck those bets then more money comes onto OKC ML, and we’ll fuck those bets too because the Pacers will lose but cover” holy shit Vegas MMs working hard just like the financial markets

Chrg88

Pacers ML. Even Bers can cash this

tomis_24

Why would you use python for data analysis? It is best for AI/ML and automating data science tasks.

OptionSellingRegard

Pacers 1 quarter ML +130 🔒

greytoc

It's great to hear about your approach to advisory services. It's always nice to know that there are people like you in the business. I think it's like most professions. There are going to be bad, mediocre, and great people. A lot of RIA's that I'm accustomed to are just pushing SMA programs and just stick their clients into an SMA. But that's probably because of the area that I worked at. And of course - there are the incredibly horrible advisors out there - like that crazy advisor that is spouting off about investing in BBBYQ and GME and spreading nonsense about naked shorting on reddit. While I don't have an advisor, my father has an amazing IAR at ML that he works with - the guy doesn't even collect an AUM since my dad's account is self-directed. But he goes way above what I would expect any IAR to do. My dad is hearing-impaired and the guy will help my dad out whenever my dad needs help with even non-ML issues at BofA. It's not even about the fees saved but about having someone that is trusted.

acadia11

Nothing … although you could build a ML model and try and infer some correlation, but unless a war is going to impact manufacturing or rare earth medals or disrupt supply chain a war say in the Middle East will have little impact, IMHO.  There could be an overall drag or pop on the markets the drive many sectors to react.  Shipping of good though could be impacted depending on how wide spread said war becomes which could drive up price of goods … which would positively impact stock prices unless it cut back on spending.  So , basically while I see no impact the crystal ball could say otherwise. 

Striking-Credit-2765

I made the lady laugh/smile in the interview. I think I’ll get this compliance internship at the bank! The role involves learning about AI/ML ethics and regulations, working with data science teams, and also understanding how AI models work. Sounds like interesting work, and the pay’s probably going to be around $32/hr. Is it gonna be valuable for career? Or waste?

ThisKarmaLimitSucks

I'm not buying it. I guarantee you what they did is use ML to train it on the past 100+ years of climate data. They don't have any new info that current models *don't* have, they're just going to overfit harder to the same info already being used.

ThisKarmaLimitSucks

What I expect they're doing is using ML to overfit it to that past data.

ShadowK2

Apple is going downhill. Their Mx architecture used to be hugely differentiated from other processors. Intel and AMD are now catching up/surpassing the Mx architecture. NVidia is just stomping on Apple in graphics and ML workflows Their phones and tablets are also losing competitive edge and they’re reduced to adding small iterative improvements and gimicky software features rather than real innovation. If Apple doesn’t produce some real innovation in the next year or two, I suspect their stock starts to erode.

the_bueg

I have, for a long time. Most of it came down to dumb luck. For example, I bough a lot of Nvidia early and cheap, and cashed it out near ATH. Not because I'm a genius, but because I got lucky. (It might still keep going up, and I'm OK with that.) I also used to work for Microsoft. Not an early employee from the 90s, so I figured I had missed all the explosive growth. But I held on to my exercised options anyway after I left, and lo and behold, MSFT has also been killing it. (The first version of this comment got autodeleted for the mere mention of certain computer-generated assets and how that was also dumb luck and no one - certainly not me - had any way of predicting what would become of it. I was just genuinely curious.) That's the problem with trying to catch "the next big thing". There are too many things that might be it, and usually no real way to know what spaghetti noodles are going to stick. And you don't want to spread yourself too thin. But I will say this: My successful ride with NVDA wasn't a TOTAL fluke. I was using nvidia products non-stop since the late 90s, when they first started trying to compete with Voodoo 3gfx. I also got into [hash-based asset creation out of thin air] pretty early, back when you could do so on GPUs. So I had a little more than a "hunch" that said asset class *might* become a big market for them. And in my field of work at the time, we were using CUDA and some of the precursor ideas and libraries to LLMs, neural nets and evolutionary ML. So I did have SOME idea that their market was going to grow. And of course I did my homework on the fundamentals. Nvidia was solid, executing brilliantly, and firing on all cylinders. I do think their explosive growth in the marketplace is more or less over, as competitors catch up. But their market cap may continue to grow without that, just on irrational exuberance. They still execute well, have solid leadership, and their PE ratio has plenty of room for TSLA-style overvalued hype. But I'd rather invest in solid growth, not hype. I've also been pretty lucky with algorithmic swing trading (my own algos). But I don't for a second believe that any of that was because I'm smart, or can time the market, predict the future, etc. I mean, I've worked very hard and put in thousands of hours into swing trading and programming. But that's more just hard work and rigid discipline, more than being some kind of market genius. TLDR: Yes, through hard work, discipline, but mostly dumb luck. I should also add, in the spirit of transparency, that I got *slaughtered* in the dot-com bust. I did really well in the run-up, as it was impossible for even a blind muskrat to throw a bad dart. (I did well first with my own company, then working for an actually pretty solid startup that survived to this day, with generous stock options. I was actually pretty disciplined with diversifying and not blowing it all on stupid stuff, but still lost it all.)

Greenappleflavor

How are your ML accounts being invested? If it’s individual stocks, then I have no idea where he’s getting it from. If it’s ETFs and/or Mutual Funds, he’s probably talking about the OER/fund fees and I’m assuming combined with the advisory fee, you’re paying over 2%. TIAA isn’t much better tbh. But I’m assuming that’s what he’s talking about since OERs are built in fund fee (operating expense ratio) and there might be a front/back load (rare, but still in existence) fee too. He should be able to explain it clearly to you as I have. If not, then yes, he’s probably early on in his career and more salesy than value oriented at this point.

the_niles_crane

Unless it’s an ML SMA, there is a fee. No manager would do this for free. That said, the fee is probably pretty reasonable.

Cashsteven111

import yfinance as yf import pandas as pd import numpy as np from sklearn.ensemble import GradientBoostingClassifier from sklearn.metrics import accuracy_score, confusion_matrix import matplotlib.pyplot as plt # Step 1: Download historical data df = yf.download("SPY", start="2015-01-01", end="2024-12-31") df.dropna(inplace=True) # Step 2: Feature Engineering df['Return'] = df['Close'].pct_change() df['Lag1'] = df['Return'].shift(1) df['Lag2'] = df['Return'].shift(2) df['Lag3'] = df['Return'].shift(3) df['MA10'] = df['Close'].rolling(10).mean() df['MA50'] = df['Close'].rolling(50).mean() df['Momentum'] = df['Close'] - df['Close'].shift(10) df['Volatility'] = df['Return'].rolling(10).std() df['Target'] = (df['Close'].shift(-1) > df['Close']).astype(int) df.dropna(inplace=True) # Step 3: Split data features = ['Lag1', 'Lag2', 'Lag3', 'MA10', 'MA50', 'Momentum', 'Volatility'] X = df[features] y = df['Target'] split = int(0.8 * len(df)) X_train, X_test = X[:split], X[split:] y_train, y_test = y[:split], y[split:] dates_test = df.index[split:] # Step 4: Train model model = GradientBoostingClassifier(n_estimators=100, max_depth=3) model.fit(X_train, y_train) y_pred = model.predict(X_test) # Step 5: Simulate strategy returns df_test = df.iloc[split:].copy() df_test['Pred'] = y_pred df_test['StrategyReturn'] = df_test['Return'] * (df_test['Pred'] * 2 - 1) # +1 for long, -1 for short # Step 6: Calculate performance cumulative_return = (1 + df_test['StrategyReturn']).cumprod() benchmark_return = (1 + df_test['Return']).cumprod() win_rate = accuracy_score(y_test, y_pred) print(f"🏆 Accuracy: {win_rate:.2%}") print(confusion_matrix(y_test, y_pred)) # Step 7: Plot returns plt.figure(figsize=(10, 5)) plt.plot(cumulative_return, label="Strategy") plt.plot(benchmark_return, label="SPY Buy & Hold", linestyle='--') plt.legend() plt.title("ML-Based Strategy vs SPY") plt.grid(True) plt.show()

Cheeto-2020

Yeah, ML says there's no additional SMA fee, only the advisory fee. And the advisory fee is very clearly marked on the statements, so there's no confusion there.

DoinIt4DaShorteez

There's no secret fee. You have to sign a contract and the fee is negotiable. "You may elect to have the MAS Fee paid by debit from the MAS Account, debit from an alternate Merrill account, or through the payment of an invoice." "Deduction of Account Fees. As set forth in the Client Agreement, you may authorize us to deduct the MAS Fee from your Account. We also may, for your convenience and if so agreed between you and your Investment Manager, deduct from your Account the Investment Manager Fee" If you didn't pay it by check, there would be a line item(s) on your statement. https://mlaem.fs.ml.com/content/dam/ML/pdfs/MAS-ADV.PDF Starts on page 15.

the_niles_crane

I agree with this. You will see the fee deductions each quarter on your statement. TIAA sells hard and I have run into more than one unprofessional rep there. You can also ask ML what the SMA fee is. I suspect it’s an easy ask and the fees aren’t that high. With an SMA, there’s really no way to hide fees, because they are deducted straight from that account.

OptionSellingRegard

Panthers ML it is

putsellingregard

Pacers 1Q ML $250 let’s go

doplitech

Go watch “nvdia your late” video on YouTube by Alex ziskind. He actually runs benchmarks on the new ai chip, and open models. Needless to say it still seems that m4 max and pro chips are way better suited for ML and local LLMs.

Zephos65

The distance between academic and production is small (within ML). Oftentimes nowadays, a product release is joined by a paper being pre-printed on arxiv

_hiddenscout

Agreed. I think google's deep mind is pretty underrated. It figured out protein folding. That's the thing with AI, there are LLM's and ML. I think there is much more ROI in the ML side of things.

Domitiani

This is the correct take in my opinion. Additional with the prevalence of AI/ML trading currently there is a stronger driver of AI identified pattern trading which further reinforces TA. That said, from what I've seen TA is only really useful in trading on "no news" (or rumors) - as others have said it is only a view of history and leaves you exposed to unexpected news/swings. A lot of people have been burned on this lately with the tweet-driven stock market,

Classic-Finish-7433

Lost $1200 on FSU baseball (-1.5) parlay with FL panthers ML last Friday night when they were leading 4-1 with 2 outs in bottom of the night. Following it up with a Seminoles natty futures play while backing the $13 parlay kid https://preview.redd.it/jk1ku2h7y66f1.jpeg?width=1179&format=pjpg&auto=webp&s=a56f0a33a9ebcbd52aba22484a7c0d35ff3eea15

SuperSultan

Is it actually profitable for Intel to design and manufacture GPUs? Do they have a niche (Nvidia has training whereas AMD claims inference in AI/ML)? Which crowd is primarily buying Intel GPUs? Do they sell out quick? Is there a lot of demand but little supply? I would think about these points before buying Intel over GPU news.

Zephos65

I'm a machine learning researcher and something I don't often see talked about is that FAIR (Facebook AI Research, no idea what it's called now) is one of the best AI labs you could be in. Facebook created pytorch, which virtually all ML is based on. They created flow matching, which is where state of the art image generation is at. They publish a ton of papers every year and are at the cutting edge of every subfield of machine learning.

Initial_Ad_9250

"Research AI and ML " anywhere between maintaining big ass excel sheets to preparing endless ppts for the regards in upper management

2nd_yr_cs

I got an interview from a bank, role says compliance model oversight! What does compliance mean? What work do they do? Does it experience have value for future career? Says I need to research AI and ML

kradproductions

Re: your $5 PS cleanup job.... It's called runway ML you cheap bastarrd. You're welcome, A "triggered" American.

xploeris

> The bull trap is people truly believing AI will slow walk us into the Star Trek future But where is the bear case? Look, Bitcoin is bullshit. It still made people who should know better a shit ton of money, while the people who saw it for the scam it was sat on the side and lost. Now AI (really ML, LLMs - not actual AI at all, and deeply fallible) is bullshit. And it's raking in money for the people who are riding it up. So what makes AI crash? Even if it turns out not to fulfill the most fevered sci-fi dreams of the true believers, what makes investors drop it like flaming dogshit? How do AI companies crash?

Civil_Musician_9521

I am 32 years old. Due to financial regulations, I was only allowed to have an archaic Merrill Lynch account. Thankfully, as of yesterday, I am able to break free and move to Fidelity. I felt very constrained at ML. Now that I am able to start fresh and actually invest a lump sum of money in fractional shares of ETFs and mutual funds, I am trying to figure out the right diversification. I plan to invest about $1,000 a month into the following: 35% into FTEC 35% into SPLG 25% into FCNTX Is that a good mix? (I am aware some of these funds will have overlap in stocks and sectors.) I also plan to contribute an extra $50–$100 a week into FCNTX. I am really looking at that one fund as something I can grow, collect residual capital gains from, and not have to think about too much. Another fund I was considering is FOCPX in lieu of FCNTX. I am also open to seeing if I am overlooking any funds in Mid Cap Growth or Small Cap Growth or a Blend. Thanks!!

Spicyboi981

The databases already exist but are mostly contained within their dept. What’s concerning is Palantir integrating all of these databases, and feeding it into an ML model that tries to “predict” our behavior.

GureenRyuu

Yep. Even non LLM AI/ML has been a thing for a while now since big data became a thing for recommendations, algorithms, etc.. But it was hidden behind the scenes and invisible to consumers.

brain-juice

I’ve been reading a book about AI engineering. It talks about how people in the AI/ML field were able to prove/test the feasibility of LLMs and such many decades ago, but had to wait for hardware to catch up. Now that we have the hardware, the concern is that we’re already running out of data for training.

Mjolnir2000

AI predates the internet by decades. I don't know we can still say we're in the first inning. Transformer-based language models are comparatively new, but they're still built on prior ML techniques that have been studied for ages and ages, and we're already getting a pretty good idea of what the limitations are.

Vivid-Scallion-4031

ML pacers…..

ChrisFromIT

>The "fit" will happen in the cloud or on premises. The fit can take hours or days, no worries. The "predict" is what is much less intensive but also needs low latency. The correct terms are training and inference. It doesn't help talking about ML if you do not use the proper terms. As it really muddies the argument as others might interpret the words differently from what you mean. >I don't know if the lidar predict would be much more intensive than a camera predict (or perhaps less intensive). It depends on the models used. If you have the same model with a similar amount of inputs, it will run generally the same. The model itself is where most of the computational processing is required.

collector_of_hobbies

The "fit" will happen in the cloud or on premises. The fit can take hours or days, no worries. The "predict" is what is much less intensive but also needs low latency. They can always push new fits to the cars whenever. I don't know if the lidar predict would be much more intensive than a camera predict (or perhaps less intensive). I would guess similar but I haven't done enough ML models to make a good guess. Also, what ML I've been exposed to is an entirely different area.

ChrisFromIT

It still does require tight latency, as the ML stuff is what determines the action of the driverless car. Which also means it can not be in the cloud or at least enough processing needs to be in the car for it to function if the cloud is not reachable for a certain amount of time.

Expert_Lab_9654

But the ML stuff doesn’t require tight latency and can be in the cloud

ChrisFromIT

>One of the issues with using Lidar is that it is very processor-intensive It certainly isn't more compute intensive than running ML inference. That has always been the more compute heavy part of the whole system. The bigger issue is that Lidar can be fairly expensive. But a lot of startups and leaders in the space are trying to bring costs down quite a lot for the Lidar hardware.

memofor

ML went bankrupt during economy melt down in 2008. Makes for bad advertising, they said they were over leveraged. BofA bought them for Pennie’s on the dollar.

NiceToMeetYouConnor

You realize AI is an area of research that’s been around for a very long time right? Ever heard of Alan Turing? The imitation game? This isn’t some new idea, we have just slowly gotten the hardware to make it possible. Chess bots were considered AI when they first came out. To expect the “AI Boom” to mean that it’s a new technology that nobody has ever used before is naive. Let me break it down for you: AI/ML has been around for a long time and around 2010 we realized we could start going bigger with models, I believe it was AlexNet that showed this in 2012. So yes it has been a slowly growing area and did not come out yesterday but our use cases and capabilities continue to grow which is why we have a boom. In 2016 it was huge to be able to consistently classify images at a high accuracy and now we have self driving capabilities. Or on the language side we used to be able to analyze documents for sentiment or classification or basic NER and now we can communicate with large amounts of unstructured text and extract real insights. The change in technology in the last decade is tremendous and is why it’s called a Boom. You’re welcome :$

Vaun_X

It's passable as a passive investor, poor mutual fund choices, no fractional shares, poor automated sweep options. Main reasons folks use ML is if they're already with BofA or want to qualify for preferred rewards.

davecrist

They are crypto averse. ML geezers hate these crypto kiddos and wish they’d git off their lawns. And I’m pushing 60.

jozero

Oh boy  1. Apple never launches new iPhones at their developer conference in June 2. This is just a rumoured product that could take 3 to 5 years to come out 3. Not sure how Apple allowing devs to use their vastly underpowered ML models are going to set off any app purchasing wage. If anything Apple has proven on device LLMs don’t work for anything useful thus far  4. What new chips? The 2nm process won’t reduce their costs. It’ll make the chips better which is awesome but hardly reduce costs  5. What 6. Tariffs are likely going to cost a billion a quarter. They stated this  7. Ok? 8.  Yah Tim Cook basically missing the entire AI wave so he can announce record stock buy backs is visionary  9. Where you getting this from? Consumer sentiment is at a low I’m excited for Apple to turn itself around now that it doesn’t think it’s king and is running a bit scared, but I have no idea how that aligns with your list 

toyz4me

BOA / ML is just so hard to work with. IMO, the ML app is kluge, and feels 10 years behind. And it’s difficult to work with them directly. When I place a trade order I talk with 2 different people and go through the same script / questions. It literally is a 20 minute plus process

toyz4me

BOA / ML is just so hard to work with. IMO, the ML app is kluge, and feels 10 years behind. And it’s difficult to work with them directly. When I place a trade order I talk with 2 different people and go through the same script / questions. It literally is a 20 minute plus process

therealjerseytom

Absolutely. It's one of those things you don't quite appreciate until you're the one implementing some sort of ML thing.

Cultural-Camel7693

I’ve got ML but not thru BOA. I would say that it’s been tempting to jump ship for one of the big 3 recently. Fidelity I think has the best UI/UX for research. In the end, I stay with ML bc it has paid off so far. ML is actually much more retail than they were before when they were targeting high wealth only.

mikerock5678

PPP.V is Drilling the ML Project this summer in the Yukon. Historical drilling indicates 4g per tonne gold.

Iyace

What bubble? I work in tech, I’m utilizing AI every day now to reduce costs. I’m a software engineering director with a masters of CS, and a specialty in ML. What he’s saying is no different than what most people in the industry is saying. You’re not making a point.

1acedude

My algo trading bot essentially is. 10 minute chart but with 1 minute entries, every order is trail stopped. Because of the trail stop a lot of exits are taken within minutes of entry, I miss out on higher profits, but because it’s fully hands off I’m fine with that because I haven’t fully committed to coding a ML on price action to know if some trades should be held longer.

CalRippedken

Pacers ML

OptionSellingRegard

Knicks ML + Over 221 parlay $100 to win $370 fuck it

OptionSellingRegard

Pacers ML is the right play, but will there be scripted drama that will send it to Game 7…hmm Knicks +4

r_e_e_ee_eeeee_eEEEE

Army vet here. I would encourage you to consider what u/ML_Infidel said. My 1st enlistment was enough for me, and my mind (and body) were over it. I felt it was better to continue my mission in the way that was more suited to my talents (and background) in physics, math, and so on. You're embracing the suck, have fun with it.

SuffiecientSapian

I’m going heavy ATL and Cubs ML. Mets fucked me heavy losing to the fucking Rockies

FusterCluck96

Market sentiment is a huge factor. With the rise in ML tools in sentiment analysis, we can extract value faster and easier than before. And it has been shown to be a very valuable predictor of the market. If you think of it it makes sense. How many blue collar investors know the correct procedures for technical analysis. Many are being swayed by the "DD" posted online, particularly Reddit. There's quite a few recent academic papers on this exact topic.

_hiddenscout

We've had ML learning for a lot longer than 2022 as well. I still think we are way further away from humanoid robots than the next few years. This is a great podcast on it: [https://www.wsj.com/podcasts/wsj-the-future-of-everything/humanoid-robot-startups-are-hot-this-ai-expert-cuts-through-the-hype/4911ec21-54e6-4137-8a1a-fa4feca4ff25?gaa\_at=eafs&gaa\_n=ASWzDAhvPLjf0R2kgkN4KkiKza30vaope6\_KfP47oDKUoT2z-dKx0874GtnwvNYeAQo%3D&gaa\_ts=68388236&gaa\_sig=ZmKxjI9\_Wew7-pMgJ97\_LoDo8pEJHWArwzH4PmRujF-MhPf5jX2oB3CGdmnK1YFes1NO0e8g7YKvO74VfJcfVg%3D%3D](https://www.wsj.com/podcasts/wsj-the-future-of-everything/humanoid-robot-startups-are-hot-this-ai-expert-cuts-through-the-hype/4911ec21-54e6-4137-8a1a-fa4feca4ff25?gaa_at=eafs&gaa_n=ASWzDAhvPLjf0R2kgkN4KkiKza30vaope6_KfP47oDKUoT2z-dKx0874GtnwvNYeAQo%3D&gaa_ts=68388236&gaa_sig=ZmKxjI9_Wew7-pMgJ97_LoDo8pEJHWArwzH4PmRujF-MhPf5jX2oB3CGdmnK1YFes1NO0e8g7YKvO74VfJcfVg%3D%3D)

Big-Fat-Sak-53

If you want an easy double take Florida ML tn. They had injuries g4 and this is just free money cause those guys are back nhl

noClip2

Canes ML. Guaranteed win. Panthers have injury issues

skilliard7

>Anyone betting against Google because of AI has no understanding of AI. I've worked with neural networks/ML/AI and I still think Google is at risk. There is 2 components to search; the back end search algorithm, and the presentation to the user. OpenAI may be using Bing for its back end search, but AI interacts with the results to present it to the user in a more convenient way. The risk to Google is that AI competes on the front-end experience. When I use Google, I may have to scroll through several ads to get to the page I want to read, possibly clicking on them, making Google money. But with AI, the expectation is that I get what I want directly. This completely threatens Google's entire business model, even if they still have the best search engine on the back end. ChatGPT search achieving great results despite using Bing also isn't a good sign for Google. It suggests that Bing is not far behind Google on the back-end, but Google's dominance is more due to anticompetitive practices, such as paying Apple/Mozilla to be the default engine. There is no reason for OpenAI to reinvent the wheel and develop their own search engine this early on, when Bing works well. They are better off focusing their capital on improving their models and expanding their capacity.

leroy_hoffenfeffer

I work in AI/ML and mostly don't use Google at all anymore except for looking for restaurants on maps or something like that. Search is straight dog shit. If you're going to give me AI summaries for simple shit, I'll just go ask chat gpt, which usually provides better results.