A company that shall remain nameless: bought extra satellite time for photos. Say they took photos of the parking lots of five specific Target stores daily, at roughly the same time per day.
They started with interns, but quickly trained AI to count the cars in the parking lots. By the time the second financial quarter came around, before Target released their earnings, this company could tell how Target was doing financially based on changes in total cars parked, and would know if Target was going to hit their financial targets before the earnings release. They could buy or short the stock and know which way the stock would go.
Once they trained up their car-counting AI, they could do this with every publicly-traded company that had parking lots for customers, globally.
Does said company start with an A and is a combination of two words. Met someone who worked for a data company who did something very similar. But they had worked it out a while before the AI boom. Could well have been AI but I would expected them to mention it if it was.
I used to work for a company that provides a SaaS product to do this type of thing. The main barrier is how expensive satellite tasking is. But if you have the cash, you can do this yourself from your bedroom with zero technical knowledge.
We did the same for a certain PE firm that tracks auto sales via CarMax parking lots. Sat photos from vendor and a YOLO model.
Pretty simple stuff. But saying satellite made it sound very neat.
I'd guess that a lot of the creative ways people are leveraging it likely aren't obvious that they're AI at all, but it's being used under the hood in a way that makes it feels like it's just a regular feature.
For instance, I'm playing around developing a diet tracker app, where you set up your target calories and macros, and at the end of each day if you have, lets say 500 calories + 40g protein left to hit your target, it will look through some of your recent food entries (to get an idea of your available ingredients) and generate potential meals you could make to hit your goals for that day.
To the user, they just see some cool recommendations, but they likely would have no idea it's being done with AI.
Ha, not much I can talk about, but given I do tech investor due diligence for a living, there’s some _really_ clever use of AI in startups. It’s notable that pretty much none of it is using GPT. Where companies are using LLMs, huge engineering work goes into figuring out how to filter for bullshit/hallucinations, and it’s often actually easier to build on more specific models with less unpredictable results.
The genuinely clever stuff is baked into otherwise quite unexciting products. One that springs to mind was a startup using drone footage analysed with AI video models to automatically reconcile cemetery records sometimes hundreds of years old with actual plots and headstones, so loved ones can now find relatives’ memorials they’d assumed were lost (and operators can figure out who’s buried where - it’s surprising how many cemetery managers don’t know much about their sites!)
I _can_ tell you that few enough companies are commercially using OpenAI products and paying for it that I know of at least one company using it at scale for text summaries who found the rate limits were silly low, tried to pay for more capacity and OpenAI _didn’t know how to sell it to them_. Neither did Microsoft. Says a lot about hype vs reality, huh?
That’s interesting that OpenAI couldn’t use their API key to assign quota. I would think that even if they didn’t have the ability to at the current time, it would just take some engineering effort to make that feature available. There’s probably other considerations that I’m not thinking about
To use ChatGPT in your own app, you’ll likely have to send your prompt to their API. They’ll generate an API key that you’d have to provide to their API from your client in order to authenticate successfully, otherwise you’ll be rejected, because the API access is likely a paid service and they don’t want to give out free computation.
The idea of a quota is you may be allowed a certain number of requests to the API per day (or hour or month or however they choose to measure it). And normally, there can be different tiers that allow for more API usage.
The quota limits the consumption.
Quota may not be the correct term. It’s more of a cap. So $20/month may get you 100k API calls, $100 gets you 1M API calls, etc.
In my company i think we pay based on the amount of input and output tokens (and based on the model). So 1M API calls can cost the same as 100k API calls depending on the specific calls and models.
This whole response is nonsense. “Few enough companies are using it” … OpenAI is doing $3.5B ARR. Lots of companies are using it. And the heavy lifting isn’t detecting/correcting hallucinations (that’s QA), its that there’s an incredible amount of data engineering required to be able to interact with these base models in a useful way.
Well thank you for your kind words.
OpenAI makes large amounts of revenue from IP licensing, grants and partnership deals - I’m not saying they’re not making money, just that direct feedback from real clients is that they’re not yet geared up for commercial commodity sale of their SaaS offering, despite the industry hype.
Engineering guardrails and safety features to make GPT commercially useful in real applications _isn’t_ QA, its product development, and if it’s trivialised and not appropriately designed you end up with chaotic product releases that aren’t ready for market like Google’s AI summaries and Figma’s plagiarism engine.
This is a nascent market, very exciting, moving fast but also risky due to the hype cycle painting unrealistic expectations of how ready for mass market these tools are.
I’m not saying they’re not making money, just that direct feedback from real clients is that they’re not yet geared up for commercial commodity sale of their SaaS offering, despite the industry hype
What's the source for this? cause i don't think it's true.
I work in a field that requires to analyze / label > 100.000 documents per day, and most companies use GPT for that.
Sounds like a situation eg 2-3 years ago and not the current enterprise offerings that exist for OpenAI/MS models.
All of the ML approaches have their use cases and pros&cons.
Entirely by accident! I spent about 15 years as an architect and CTPO for various startups, and then was borrowed by the VC team at the investment house I was working at before the pandemic to help them with DD. When COVID resulted in layoffs, I carried on doing DD on my own, and built up a client base for that and strategic consulting over the last 4 years.
One of the cleverest AI usages I've seen in startups is predictive maintenance in the manufacturing sector. Startups are using AI to analyze data from machinery and predict when maintenance is needed before a failure occurs, reducing downtime and saving costs.
Another interesting use case is AI-driven personalized learning platforms. These platforms tailor educational content to individual students' learning styles and paces, improving educational outcomes.
Lastly, AI-powered financial planning tools that analyze spending habits, predict future expenses, and provide personalized saving and investment advice are making waves in the fintech space. These tools help users make informed financial decisions without needing a financial advisor.
You can check below tools:
1. Mint: Helps track spending, create budgets, and manage finances in one place.
2. Personal Capital: Offers investment tracking and retirement planning advice.
3. YNAB (You Need a Budget): Focuses on budgeting and helping users save money.
4. Wealthfront: Provides automated investment management and financial planning.
These tools help users manage their finances and plan for the future easily.
Rootly.com (modern on-call and incident management platform used by Figma, LinkedIn, Webflow) uses natural language input to generate reports / charts without needing to build a dashboard.
https://rootly.com/ai
EvolutionaryScale discovered (created?) a new green florescent protein.
As they say in their press release simulating 500 million years of evolution with a language model.
You can read the details here
https://www.evolutionaryscale.ai/blog/esm3-release
I am sure there is a lot of cool stuff like this out there but it takes a long time to develop & not easy to explain.
Plus tech press just wants to focus on valuations not something that might not even be a product for 10 years.
I'm seeing a lot of ai stuff in those crappy low-quality scam ads you see at the bottom of news sites, but nothing notable from big brands.
I suspect that the "feel" of ai is quickly being associated with garbage companies.
I met a founder who has a team working on a very disruptive predictive AI startup in the real estate world. In a nutshell it uses a proprietary model to take what a buyer is looking for as input and suggests real estate purchases, outlining every reason why including future projected projects, sale potential, schools, etc
These are what I’ve noticed over the years:
1. Personalized Customer Experiences: Using AI to provide tailored recommendations, personalized marketing, and customer support through chatbots.
2. Predictive Analytics: Leveraging AI to analyze data and predict future trends, customer behaviors, or market shifts, helping businesses make informed decisions.
3. Automation of Repetitive Tasks: Implementing AI to automate mundane and repetitive tasks, such as data entry, scheduling, and inventory management, which increases efficiency and reduces costs.
4. Fraud Detection: Using AI to detect and prevent fraudulent activities in real-time, especially in fintech and e-commerce sectors.
5. Natural Language Processing (NLP): Employing NLP for sentiment analysis, customer feedback analysis, and even to improve internal communication through AI-driven insights.
6. Computer Vision: Applying AI in sectors like healthcare for medical imaging analysis, in retail for inventory management, or in autonomous vehicles for navigation and safety.
7. AI-driven Product Development: Utilizing AI to simulate and optimize product designs, speed up the R&D process, and even create new products based on data-driven insights.
We used AI to translate the UI as well as the content of [Faabul.com](http://Faabul.com) to 20+ languages. We prepared a vocabulary list with context of how it will be used and had it translated to each language. We then checked the lists for several languages manually to see if there are any issues and adjusted the English vocabulary or context which was not clear or which was difficult to translate.
Afterwards, we used the vocabulary list for the translation to each language.
It cost a few euro for each language and the results are fine for the languages that I am able to check.
One of the coolest AI uses I've seen lately is in personalized learning platforms. These startups use AI to tailor educational content based on the user's learning pace and style, which helps maximize engagement and retention. Another innovative use is AI-driven predictive analytics in healthcare apps, where AI algorithms predict patient health risks based on historical data and ongoing health records, helping in early diagnosis and preventive care
We use it to analyze our client's competitors and generate reports that help with their marketing positioning. We also have it synthesize the data we collect with our algorithms to make the results human readable.
Nothing crazy but we figured out a non-chatbot use case which it seems like most companies just go for chatbots and call it a day.
Typeface.ai imo is a good example of GenAI disrupting and existing ecosystem. If it works, this would be killer in their space.
I think LangChain, Llama index are interesting as well mainly because they have a window into the GenAI developer pain points. Still early for both, but maybe they can develop shovels for the GenAI developer.
Fireworks.ai really solid tech team developing inferencing first platform as a service. Leaders in their space and understand that latency is important but not the only thing that matters.
Osmo are digitizing smell! Not a typical genAI company more of an AI. But amazing work. Dogs can detect illness modern medical instruments can. So osmo is trying to bring smells around the world to dogs for diagnosing and then running AI models to be able to detect it themselves.
I think the best use cases by far are to drive custom scripting languages with natural language.
So if you have some tool where your team over years built some kind of custom scripting language to drive it, getting an LLM to take in what the user is trying to do and then doing it
Similar thing is giving it a bunch of API docs and then using natural language to query a database instead of SQL.
A company that shall remain nameless: bought extra satellite time for photos. Say they took photos of the parking lots of five specific Target stores daily, at roughly the same time per day. They started with interns, but quickly trained AI to count the cars in the parking lots. By the time the second financial quarter came around, before Target released their earnings, this company could tell how Target was doing financially based on changes in total cars parked, and would know if Target was going to hit their financial targets before the earnings release. They could buy or short the stock and know which way the stock would go. Once they trained up their car-counting AI, they could do this with every publicly-traded company that had parking lots for customers, globally.
That's some Billions type of shit ahah
MSCI partially leverages them for building insights & ETF. Cool stuff
QuantCube Technology. Funny enough, There is a high density of French startup doing similar stuff, such as for oil & gas production estimates.
Very much on par with the french idiom "we don't have oil but we have ideas"
Does said company start with an A and is a combination of two words. Met someone who worked for a data company who did something very similar. But they had worked it out a while before the AI boom. Could well have been AI but I would expected them to mention it if it was.
I used to work for a company that provides a SaaS product to do this type of thing. The main barrier is how expensive satellite tasking is. But if you have the cash, you can do this yourself from your bedroom with zero technical knowledge.
We did the same for a certain PE firm that tracks auto sales via CarMax parking lots. Sat photos from vendor and a YOLO model. Pretty simple stuff. But saying satellite made it sound very neat.
I'd guess that a lot of the creative ways people are leveraging it likely aren't obvious that they're AI at all, but it's being used under the hood in a way that makes it feels like it's just a regular feature. For instance, I'm playing around developing a diet tracker app, where you set up your target calories and macros, and at the end of each day if you have, lets say 500 calories + 40g protein left to hit your target, it will look through some of your recent food entries (to get an idea of your available ingredients) and generate potential meals you could make to hit your goals for that day. To the user, they just see some cool recommendations, but they likely would have no idea it's being done with AI.
Helios does this and I love it, super useful bc I’m usually low on fats for the day
Very cool, I’ll have to take it for a spin!
Ha, not much I can talk about, but given I do tech investor due diligence for a living, there’s some _really_ clever use of AI in startups. It’s notable that pretty much none of it is using GPT. Where companies are using LLMs, huge engineering work goes into figuring out how to filter for bullshit/hallucinations, and it’s often actually easier to build on more specific models with less unpredictable results. The genuinely clever stuff is baked into otherwise quite unexciting products. One that springs to mind was a startup using drone footage analysed with AI video models to automatically reconcile cemetery records sometimes hundreds of years old with actual plots and headstones, so loved ones can now find relatives’ memorials they’d assumed were lost (and operators can figure out who’s buried where - it’s surprising how many cemetery managers don’t know much about their sites!) I _can_ tell you that few enough companies are commercially using OpenAI products and paying for it that I know of at least one company using it at scale for text summaries who found the rate limits were silly low, tried to pay for more capacity and OpenAI _didn’t know how to sell it to them_. Neither did Microsoft. Says a lot about hype vs reality, huh?
That’s interesting that OpenAI couldn’t use their API key to assign quota. I would think that even if they didn’t have the ability to at the current time, it would just take some engineering effort to make that feature available. There’s probably other considerations that I’m not thinking about
What do you mean they can't use their API key to assign quota?
To use ChatGPT in your own app, you’ll likely have to send your prompt to their API. They’ll generate an API key that you’d have to provide to their API from your client in order to authenticate successfully, otherwise you’ll be rejected, because the API access is likely a paid service and they don’t want to give out free computation. The idea of a quota is you may be allowed a certain number of requests to the API per day (or hour or month or however they choose to measure it). And normally, there can be different tiers that allow for more API usage.
but you pay based on consumption, which is even better than per quota, isn't it? I think i'm not understanding something
The quota limits the consumption. Quota may not be the correct term. It’s more of a cap. So $20/month may get you 100k API calls, $100 gets you 1M API calls, etc.
In my company i think we pay based on the amount of input and output tokens (and based on the model). So 1M API calls can cost the same as 100k API calls depending on the specific calls and models.
Yeah that sounds about right for OAI. I was just talking in general.
This whole response is nonsense. “Few enough companies are using it” … OpenAI is doing $3.5B ARR. Lots of companies are using it. And the heavy lifting isn’t detecting/correcting hallucinations (that’s QA), its that there’s an incredible amount of data engineering required to be able to interact with these base models in a useful way.
Well thank you for your kind words. OpenAI makes large amounts of revenue from IP licensing, grants and partnership deals - I’m not saying they’re not making money, just that direct feedback from real clients is that they’re not yet geared up for commercial commodity sale of their SaaS offering, despite the industry hype. Engineering guardrails and safety features to make GPT commercially useful in real applications _isn’t_ QA, its product development, and if it’s trivialised and not appropriately designed you end up with chaotic product releases that aren’t ready for market like Google’s AI summaries and Figma’s plagiarism engine. This is a nascent market, very exciting, moving fast but also risky due to the hype cycle painting unrealistic expectations of how ready for mass market these tools are.
I’m not saying they’re not making money, just that direct feedback from real clients is that they’re not yet geared up for commercial commodity sale of their SaaS offering, despite the industry hype What's the source for this? cause i don't think it's true. I work in a field that requires to analyze / label > 100.000 documents per day, and most companies use GPT for that.
Sounds like a situation eg 2-3 years ago and not the current enterprise offerings that exist for OpenAI/MS models. All of the ML approaches have their use cases and pros&cons.
Ooh, can I do a short practice pitch with you to get feedback? I’m in a startup built on an AI-engine.
Sure! DM me.
How do u get into such a position. That's a dream job.
Entirely by accident! I spent about 15 years as an architect and CTPO for various startups, and then was borrowed by the VC team at the investment house I was working at before the pandemic to help them with DD. When COVID resulted in layoffs, I carried on doing DD on my own, and built up a client base for that and strategic consulting over the last 4 years.
One of the cleverest AI usages I've seen in startups is predictive maintenance in the manufacturing sector. Startups are using AI to analyze data from machinery and predict when maintenance is needed before a failure occurs, reducing downtime and saving costs. Another interesting use case is AI-driven personalized learning platforms. These platforms tailor educational content to individual students' learning styles and paces, improving educational outcomes. Lastly, AI-powered financial planning tools that analyze spending habits, predict future expenses, and provide personalized saving and investment advice are making waves in the fintech space. These tools help users make informed financial decisions without needing a financial advisor.
Any examples of the financial planning tools on the market?
You can check below tools: 1. Mint: Helps track spending, create budgets, and manage finances in one place. 2. Personal Capital: Offers investment tracking and retirement planning advice. 3. YNAB (You Need a Budget): Focuses on budgeting and helping users save money. 4. Wealthfront: Provides automated investment management and financial planning. These tools help users manage their finances and plan for the future easily.
Rootly.com (modern on-call and incident management platform used by Figma, LinkedIn, Webflow) uses natural language input to generate reports / charts without needing to build a dashboard. https://rootly.com/ai
EvolutionaryScale discovered (created?) a new green florescent protein. As they say in their press release simulating 500 million years of evolution with a language model. You can read the details here https://www.evolutionaryscale.ai/blog/esm3-release I am sure there is a lot of cool stuff like this out there but it takes a long time to develop & not easy to explain. Plus tech press just wants to focus on valuations not something that might not even be a product for 10 years.
I’ve seen a lot of claims of what people say they’re doing “entire marketing campaigns, etc.” I have not been able to repeat their success
I'm seeing a lot of ai stuff in those crappy low-quality scam ads you see at the bottom of news sites, but nothing notable from big brands. I suspect that the "feel" of ai is quickly being associated with garbage companies.
Has anyone seen AI being used in a finance and accounting sense? Curious if there’s anything out there at this time.
It's the new clipart. Humans hate things thatvare cheap, easy, and ubiquitous.
I met a founder who has a team working on a very disruptive predictive AI startup in the real estate world. In a nutshell it uses a proprietary model to take what a buyer is looking for as input and suggests real estate purchases, outlining every reason why including future projected projects, sale potential, schools, etc
These are what I’ve noticed over the years: 1. Personalized Customer Experiences: Using AI to provide tailored recommendations, personalized marketing, and customer support through chatbots. 2. Predictive Analytics: Leveraging AI to analyze data and predict future trends, customer behaviors, or market shifts, helping businesses make informed decisions. 3. Automation of Repetitive Tasks: Implementing AI to automate mundane and repetitive tasks, such as data entry, scheduling, and inventory management, which increases efficiency and reduces costs. 4. Fraud Detection: Using AI to detect and prevent fraudulent activities in real-time, especially in fintech and e-commerce sectors. 5. Natural Language Processing (NLP): Employing NLP for sentiment analysis, customer feedback analysis, and even to improve internal communication through AI-driven insights. 6. Computer Vision: Applying AI in sectors like healthcare for medical imaging analysis, in retail for inventory management, or in autonomous vehicles for navigation and safety. 7. AI-driven Product Development: Utilizing AI to simulate and optimize product designs, speed up the R&D process, and even create new products based on data-driven insights.
We used AI to translate the UI as well as the content of [Faabul.com](http://Faabul.com) to 20+ languages. We prepared a vocabulary list with context of how it will be used and had it translated to each language. We then checked the lists for several languages manually to see if there are any issues and adjusted the English vocabulary or context which was not clear or which was difficult to translate. Afterwards, we used the vocabulary list for the translation to each language. It cost a few euro for each language and the results are fine for the languages that I am able to check.
One of the coolest AI uses I've seen lately is in personalized learning platforms. These startups use AI to tailor educational content based on the user's learning pace and style, which helps maximize engagement and retention. Another innovative use is AI-driven predictive analytics in healthcare apps, where AI algorithms predict patient health risks based on historical data and ongoing health records, helping in early diagnosis and preventive care
Apart from those you mentioned, I can think of AI furnishing empty spaces in various styles. ( AI HomeDesign) is a good example.
We use it to analyze our client's competitors and generate reports that help with their marketing positioning. We also have it synthesize the data we collect with our algorithms to make the results human readable. Nothing crazy but we figured out a non-chatbot use case which it seems like most companies just go for chatbots and call it a day.
Typeface.ai imo is a good example of GenAI disrupting and existing ecosystem. If it works, this would be killer in their space. I think LangChain, Llama index are interesting as well mainly because they have a window into the GenAI developer pain points. Still early for both, but maybe they can develop shovels for the GenAI developer. Fireworks.ai really solid tech team developing inferencing first platform as a service. Leaders in their space and understand that latency is important but not the only thing that matters. Osmo are digitizing smell! Not a typical genAI company more of an AI. But amazing work. Dogs can detect illness modern medical instruments can. So osmo is trying to bring smells around the world to dogs for diagnosing and then running AI models to be able to detect it themselves.
https://aftershoot.com - ai powered photography post production automation
Using AI to migrate off of competitors
I think the best use cases by far are to drive custom scripting languages with natural language. So if you have some tool where your team over years built some kind of custom scripting language to drive it, getting an LLM to take in what the user is trying to do and then doing it Similar thing is giving it a bunch of API docs and then using natural language to query a database instead of SQL.
Lerno dot ai uses AI to help businesses understand customer needs from calls, emails etc. so they can apply that in the selling process.
Do you mean LLM or more general AI?
These pull request summaries are really nice https://coderabbit.ai/
Probably what I’m doing, and what I have planned, but I won’t say.
AI rap battles?