only for now. but not in the near future. it will solve entire problems by itself. like for example if an artist and a team of artists work together to create a photorealistic 3D image based on planning, would at some point be considered an entire problem. but now top models can get photorealistic outputs in seconds, where the picture is indistinguishable from an actual photograph. so it replaces the hours of work and problem of an artist or an entire group of artists, and finishes it faster than them
@@businessmanager7670 No, that's just your 2 cents prediction, which is probably only applicable to very simple use cases, the first problem being how do you input what you really want (that part is ironically commonly called "code") and then the result also have to match that, which is far from actually being accurate, also needs to be coherent with the full picture which is another challenge Same for your artistic example which may not suit the standards the team is aiming for, producing an output doesn't mean producing an output you like, unless you're speaking about simple use cases and aren't picky at all.
@@heroe1486 not it can be applicable in complex cases too depending on how much better the ai systems get in intelligence. we have only Seen improvement and increase in intelligence and not the other way around. so my prediction is based on Evidence. and for the 2nd point you made, sure when subjectivity is involved then it is not necessarily the case that one will like the output produced by the ai although it may accomplish the tasks that you intended, but that's in the same exact way that another human who is smart can make something for me and although his product accomplishes my task, i may not necessarily like it or the way it works. LOL
As an AI engineer with an experience of running businesses I feel the need to comment. I think saying " you should differentiate by building a more complex solution" is not necessarily a good advice. Many times you will find people that sell very naive and simple software and printing money because they have really good distribution. You can also find an amazing piece of software without clients because of very poor distribution and go-to-market strategy. I think the best scenario is when you have differentiation in your software AND good distribution. Which is what you're doing by creating content on your youtube channel about your AI solution ;)
Given that Open AI just killed a bunch of startups and the dreams of people hoping to make a quick buck, this advice is very timely. Well said, excellent advice that really hits home.
I've heard this wording so much, but they didn't kill anything. Those startups were not sound from the very start. Basing your product on the single API of another company to do most of the work with no exit plan is incredibly risky for a business.
@@brainmaxxing1agreed, it's basically just repackaging and selling for a higher price. Even in the non software market, that's incredibly risky, especially if you only have 1 product from 1 supplier!
Yeah, this wording misrepresents what’s been happening here. OpenAI absolutely should continue to improve and iterate upon ChatGPT. The fact that an entire startup can be ‘killed’ by the addition of a feature should be the tell here
In my experience, 90% of what customers are asking for is “put a chatbot on my website”. They’d rather drop a few cents per interaction and throw the problem at OpenAI APIs, than pay a dev to setup and maintain a chain, do fine tuning, train a model etc. If it ever scales up, then the value add would be optimization. But in the case of building a product business around AI, I totally agree with you.
Well that’s a good example of a model which is not worth training yourself: chat. It’s fast enough, probably cost enough due to the speed at which a user can ask questions and definitely worth it (it’s cost effective vs paying an agent)
I built my AI API on top of vector Databases with zero-shot classfication to determine which AI model to use for the specific task.. You can pass label variables such as ["Greetings","Coding task","Search query"] . This can reduce the cost of running your AI chatbot service. Plus if you design a smaller local model that knows when it doesn't know the answer you can outsource to a different model with zero-shot classification.
As someone who has been writing software for 3 decades, I agree with your approach. You can’t just throw AI at a problem. Automation is more than just AI.
I love that you encourage being conservative about AI usage. I feel like way too many projects have AI just because it's trending, without considering the utility vs computational cost / determinism.
This is always the way with trending technologies and wonder how many developers out there still wade through hell every single day because someone decided their tiny startup needs to be powered by microservices for no good reason.
Love your tone, speed, clarity, style of presentation and most importantly, confidence in your voice because you have done it yourself and did a great job. Kudos to you Steve !!
Already read the blog post this morning, found it very good ! So good that, when I saw the video on my youtube feed, I click on it to be sure to not miss any infos ! Great work 🔥
Excellent video. One issue you haven't mentioned is explainability (even though it's related to debuggability). Very important and becomes nearly impossible with a big black box model.
Was having the same thoughts, was thinking about some AI projects and when searching on Google to see if it has already been done there were always plenty of results who were just basically an input form and a submit button to feed it to GPT3/4 API and get a basic response. It's all about tge whole pipeline to get something useful and original, thanks for the real world showcase
Great video! Adding your own value is what differentiates a good business from a poor one! How did you train the image recognition model to the point it’s accurate without the very pricey GPU power needed for training an accurate model? Is the more specific your problem is, the less data you need to train it?
Hey man great video, but when editing and/or cutting out filler words can you cut more of the dead space as well? The dialogue ends up with weird pauses every 10 or so syllables
"Use the location of the image as output data, and the screenshot of the image as the input data" 🧠. Great video as always. Would you be able to expand a bit on what your LLM do btw? I'm still not sure what happens between the "initial code" and the "customized code", I haven't tried Mitosis, but I'm assuming it generates good enough code? 🤔 Mitosis itself is pretty interesting, the fact that you transform your generic code into multiple frameworks 👌✨
Nice video! In my project I use only one LLM but I do a lot of embedding beforehand, what are your thoughts on this approach? It is worth noting that my input output is entirely text based, usually one paragraph both ways.
Awesome. I think exactly like you about how to use AI to develop inovative products. Can you detail a little more which model you used as a base and some steps on how to train it?
One of the most insightful talks with example that I have seen on AI and productisation. This aligns with the books and talks that I have consumed over the years, most notably from Andrew Ng's lectures and textbook. For all those reasons precisely outlined in the talk, it's first principle to see any system composed of specialised components, a philosophy Unix itself demonstrate. Thank you for all your finding and sharing!
FYI there is a failure of direct retrieval with GPT-4 using the new OpenAI Assistant API. GPT tokenizes text and creates its own vector embeddings based on its specific training data. The new terms and sequences may not connect well to the pretrained knowledge in GPT's weight tensors. There was no semantic similarity between the new API terms and GPT's existing vector space. This is a fundamental issue with retrieval augmentation systems like Rag - external knowledge is not truly integrated into the model's learned weights. Adding more vector stores cannot solve this core problem.
very good point. hadn't really thought of indexing like that before. Its kind of like a memory cache that stores extra info on top of the LLM, but its not permanent and needs to be added to any request sent to the model
that was amazing, thank you! definitely going to be checking in on all your other videos! keep it up! *cheers! might need some advice tbh... but shall see... thank again!
thanks for the info.. i'm thinking that they are not charging you for how much data the model is trained on, cause it doesn't affect inference cost.. but i'm not sure maybe they amortize in the training cost
I do the same approach. Your tool has some good auto responsive, but others are really bad. I would suggest that instead of trying responsive directly, try to generate first a wireframe from the component and try to do the responsive there and convert back. The wireframes are easier to do responsive, and there are a lot of examples out there. Also, converting to wireframe I think it is an easy task. You can find opensource plugins for auto wireframe or plugins for skeleton and start from there. Thanks for sharing your thoughts.
This cleared so much confusions! i'm gonna refer this video as one of the best Videos on AI build.I have an incredible passion for genearative AI. Your words are gem.
I could also see a benefit to creating a "toolchain" with only certain parts using AI as the ability to override that AI if it makes a mistake. I think one of the biggest flaws of AI is that while it can produce incredible results, it is never "guraranteed" to work in the same way that a lot of normal algorithms can be guaranteed to work through rigorous logic. This is why AI is best suited for subjective applications where you really need to understand user's intent in a way not possible with normal code. By using AI only in specific points, it opens the way for a user to more easily tweak the results to their liking. For example, in the website builder product, if a full stack AI produced a website which combined images in an awkward way the user didn't intend, the only recourse would be to either go in and fully edit the code to fix the issue, or to edit the FIGMA model and hope the AI interpreted it better the next time. But with the AI being localized to the specific selection of images, it would probably be possible to have a manual "merge elements into image" function which would specifically override the AI's decision about which elements combine to form an image, and then plug this result into the rest of the model which can create this image in the resulting code. I've been saying for a while that while AI is really useful as a tool, it is not always the most accurate and any AI product needs to consider what AI is good at, what it might get wrong, and how best to implement it in a way that allows the user to check/fix the result of the AI if needed.
That's what im trying to tell everybody but people just seem too invested in it, from professors to students, to random business people, if AI takes over, there will be no Quality, especially in software
I have to say this is such an effective way to market your product. Offering legitimate advice and not just trying to beg people to be interested in it. Or maybe it’s not even your intention to market it because I don’t know if people who would watch these videos are in the UI / UX space but either way it comes across as genuine and helpful!
To make sure that I understand your point, you mean to avoid only connecting with LLM such as open AI but to build a unique customized product by keep training the machine to reach the fine tuning level of product right?
This is absolutely amazing. And you're 💯 correct. I am building a product myself, and yes, I have learnt recently myself, that let alone custom trained models, even GOFAI (Good old-fashioned AI) or expert systems can often get a job done in a fraction of the time. So yes, I guess this video just gave me more confidence in my own approach. I'll absolutely try to build what I can from scratch before going to the API call. All the potential pitfalls you highlighted such as performance and cost are absolutely essential to consider for my case, since I am building for a 3rd world market and a real-time use case. Thanks a lot. You're awesome to share this. This was an the easiest subscribe of the year for me. Keep it coming my man. You rock.
Requirements analysis is crucial in all systems, but in GenAI it seems obligatory to not pack the solution with high costs of model. Choosing good model and defining the usage from functionality point of view is most important.
Think of AI as an improved querying engine. It's *so* much improved that you could integrated much more closely to the overal system (unlike something like classical search engines).
I'n my own use case, I really really really miss the times when a search engine would JUST search for the words I type... things started to go astray when the default combiner became OR, not AND and by the time they've added all these "clever" algorithms to substitute my words for more "popular" choices and give me content that some pattern-matching engine has determined is related to my words... search engines have became worse than useless.
@@edgeeffect That's without even mentioning all the crap that comes in the first page which is basically the equivalent of those boring medium articles in programming but also for every other subject
Given that events that have occured in the last few days im now very weary of using OpenAI going further. With Sam Altman's departure the company seems like it could fail miserably now, and microsfot has just brought him in to build in house tech.
"don't use AI as long as possible"
nailed it. use AI as a tool for solving specific sub-problems when needed. not for the entire problem
Thank you for the translation :D I dont know but I didnt get his point
Right, defer design decisions until you need to make them. Uncle Bob would be proud.
only for now. but not in the near future. it will solve entire problems by itself. like for example if an artist and a team of artists work together to create a photorealistic 3D image based on planning, would at some point be considered an entire problem. but now top models can get photorealistic outputs in seconds, where the picture is indistinguishable from an actual photograph. so it replaces the hours of work and problem of an artist or an entire group of artists, and finishes it faster than them
@@businessmanager7670 No, that's just your 2 cents prediction, which is probably only applicable to very simple use cases, the first problem being how do you input what you really want (that part is ironically commonly called "code") and then the result also have to match that, which is far from actually being accurate, also needs to be coherent with the full picture which is another challenge
Same for your artistic example which may not suit the standards the team is aiming for, producing an output doesn't mean producing an output you like, unless you're speaking about simple use cases and aren't picky at all.
@@heroe1486 not it can be applicable in complex cases too depending on how much better the ai systems get in intelligence. we have only Seen improvement and increase in intelligence and not the other way around.
so my prediction is based on Evidence.
and for the 2nd point you made, sure when subjectivity is involved then it is not necessarily the case that one will like the output produced by the ai although it may accomplish the tasks that you intended,
but that's in the same exact way that another human who is smart can make something for me and although his product accomplishes my task, i may not necessarily like it or the way it works.
LOL
As an AI engineer with an experience of running businesses I feel the need to comment. I think saying " you should differentiate by building a more complex solution" is not necessarily a good advice. Many times you will find people that sell very naive and simple software and printing money because they have really good distribution. You can also find an amazing piece of software without clients because of very poor distribution and go-to-market strategy. I think the best scenario is when you have differentiation in your software AND good distribution. Which is what you're doing by creating content on your youtube channel about your AI solution ;)
Given that Open AI just killed a bunch of startups and the dreams of people hoping to make a quick buck, this advice is very timely. Well said, excellent advice that really hits home.
I've heard this wording so much, but they didn't kill anything. Those startups were not sound from the very start. Basing your product on the single API of another company to do most of the work with no exit plan is incredibly risky for a business.
@@brainmaxxing1 or as we say, colloquially, those people were idiots
@@brainmaxxing1agreed, it's basically just repackaging and selling for a higher price. Even in the non software market, that's incredibly risky, especially if you only have 1 product from 1 supplier!
Yeah, this wording misrepresents what’s been happening here. OpenAI absolutely should continue to improve and iterate upon ChatGPT. The fact that an entire startup can be ‘killed’ by the addition of a feature should be the tell here
What did they do??
In my experience, 90% of what customers are asking for is “put a chatbot on my website”. They’d rather drop a few cents per interaction and throw the problem at OpenAI APIs, than pay a dev to setup and maintain a chain, do fine tuning, train a model etc. If it ever scales up, then the value add would be optimization. But in the case of building a product business around AI, I totally agree with you.
Well that’s a good example of a model which is not worth training yourself: chat. It’s fast enough, probably cost enough due to the speed at which a user can ask questions and definitely worth it (it’s cost effective vs paying an agent)
I built my AI API on top of vector Databases with zero-shot classfication to determine which AI model to use for the specific task.. You can pass label variables such as ["Greetings","Coding task","Search query"] . This can reduce the cost of running your AI chatbot service. Plus if you design a smaller local model that knows when it doesn't know the answer you can outsource to a different model with zero-shot classification.
Wow! Super impressed with this video. It's an ad masquerading as an educational video, but it's done so well I don't even mind. Great information!
Yeah. If all ads were this informative, I'd watch more ads ;-)
As someone who has been writing software for 3 decades, I agree with your approach. You can’t just throw AI at a problem. Automation is more than just AI.
I love that you encourage being conservative about AI usage. I feel like way too many projects have AI just because it's trending, without considering the utility vs computational cost / determinism.
This is always the way with trending technologies and wonder how many developers out there still wade through hell every single day because someone decided their tiny startup needs to be powered by microservices for no good reason.
Hot take: Don't overkill your apps with #AI just for the sake of calling it an "AI app/company".
yeah, even a little bit of AI is enough.
Business people love buzzwords. The real issue is hiring vapid business people instead of engineers to be CEOs.
Wtf, why did you write it as a hashtag?
Love your tone, speed, clarity, style of presentation and most importantly, confidence in your voice because you have done it yourself and did a great job. Kudos to you Steve !!
I like how his promoting his business and making valuable content with really help information at the same time.
This made me think in a whole new perspective, thanks for making this video.
Please make a video on how make your own LLM or AI model.
Google's Rules of Machine Learning #1: Don’t be afraid to launch a product without machine learning.
Already read the blog post this morning, found it very good ! So good that, when I saw the video on my youtube feed, I click on it to be sure to not miss any infos !
Great work 🔥
Thanks for this. Its still over my head to actually implement but definitely saving for later when im ready!
This video was very well put together. Really liked your articulation.
Great content Steve. This is really an honest opinion on LLMs. Thanks for sharing.
great insights! especially when you give example how to train your own model to build websites.
Love it! Side question, what is the tool you use to build the diagrams for this video?
Excellent video. One issue you haven't mentioned is explainability (even though it's related to debuggability). Very important and becomes nearly impossible with a big black box model.
Was having the same thoughts, was thinking about some AI projects and when searching on Google to see if it has already been done there were always plenty of results who were just basically an input form and a submit button to feed it to GPT3/4 API and get a basic response.
It's all about tge whole pipeline to get something useful and original, thanks for the real world showcase
Thanks Steve for the free advice ! your awesome !
Excellent stuff, mate. Thanks for sharing your experience.
Thank you Steve that's a great vid ! Will you show us how to do that ?
I appreciate you actually revealing your business approach
🤡s
Great video! Adding your own value is what differentiates a good business from a poor one!
How did you train the image recognition model to the point it’s accurate without the very pricey GPU power needed for training an accurate model? Is the more specific your problem is, the less data you need to train it?
Thank you, as a newcomer to this field. Your shares help me a lot 😎
4 minutes into the video, I captured the URL and sent it to my development team and said. "Stop what you're doing, watch this. This guy knows stuff."
there were many great videos teaching ai, yet this was the most useful one!! that's a truly remarkable difference..
Hey man great video, but when editing and/or cutting out filler words can you cut more of the dead space as well? The dialogue ends up with weird pauses every 10 or so syllables
"Use the location of the image as output data, and the screenshot of the image as the input data" 🧠.
Great video as always.
Would you be able to expand a bit on what your LLM do btw? I'm still not sure what happens between the "initial code" and the "customized code", I haven't tried Mitosis, but I'm assuming it generates good enough code? 🤔
Mitosis itself is pretty interesting, the fact that you transform your generic code into multiple frameworks 👌✨
Nice video! In my project I use only one LLM but I do a lot of embedding beforehand, what are your thoughts on this approach? It is worth noting that my input output is entirely text based, usually one paragraph both ways.
Finally, someone said it. Nailed it with this one Steve🔥
I'm just blown away. Great work!
Pure gem as always, love it! Thank you Steve!
Awesome. I think exactly like you about how to use AI to develop inovative products. Can you detail a little more which model you used as a base and some steps on how to train it?
Everything said is absolutely on spot and excellently told of. But. Man, just using that OpenAI API is so much easier.
This had some great insight, thank you for that :)
One of the most insightful talks with example that I have seen on AI and productisation. This aligns with the books and talks that I have consumed over the years, most notably from Andrew Ng's lectures and textbook. For all those reasons precisely outlined in the talk, it's first principle to see any system composed of specialised components, a philosophy Unix itself demonstrate. Thank you for all your finding and sharing!
You may want to take a look at my auto-batching compute router, and the agent-os API as well
Love this! A fantastic, well-thought-out opinion. Thanks for sharing!
Thanks Steve. Great video. While listening it boosts my confidence and makes me calm.
FYI there is a failure of direct retrieval with GPT-4 using the new OpenAI Assistant API. GPT tokenizes text and creates its own vector embeddings based on its specific training data. The new terms and sequences may not connect well to the pretrained knowledge in GPT's weight tensors.
There was no semantic similarity between the new API terms and GPT's existing vector space. This is a fundamental issue with retrieval augmentation systems like Rag - external knowledge is not truly integrated into the model's learned weights. Adding more vector stores cannot solve this core problem.
very good point. hadn't really thought of indexing like that before. Its kind of like a memory cache that stores extra info on top of the LLM, but its not permanent and needs to be added to any request sent to the model
Thanks for the video , looking to learn how to do what you did in the video , where can i find more information
that was amazing, thank you! definitely going to be checking in on all your other videos! keep it up! *cheers! might need some advice tbh... but shall see... thank again!
Great video. I got a bit confused where you said you could use Google's vertex ai, in which part of your project would that be useful?
This is the most interesting video concerning creating AI apps that i've seen in my life, thank you Steve :D
This is one of the most underrated videos I have seen in a while. The breakthrough at @9:30 was awesome! Keep it up.
Quality insights with great examples. Fantastic video 😊
This is great advice, wish more people see this before it is too late!
thanks for the info.. i'm thinking that they are not charging you for how much data the model is trained on, cause it doesn't affect inference cost.. but i'm not sure maybe they amortize in the training cost
you are definitelly right !! thats the way to go, awesome job you did here
I'd love to know what software you are using for drawing your attractive system models.
Incredible video. Thank you for the insights 🙏🏼❤️
Great video and explanations, subscribed!
question where did you forked your LLM model, i mean a generic LLM where we can start training it to focus on specific problem or task?
I usually don’t comment on videos, but man you nailed it! Thank you 🙏
I've been saying this for months. That you need to use multiple custom models in combination. Easier said than done though
I do the same approach. Your tool has some good auto responsive, but others are really bad. I would suggest that instead of trying responsive directly, try to generate first a wireframe from the component and try to do the responsive there and convert back. The wireframes are easier to do responsive, and there are a lot of examples out there. Also, converting to wireframe I think it is an easy task. You can find opensource plugins for auto wireframe or plugins for skeleton and start from there. Thanks for sharing your thoughts.
That was really insightful. Thank you.
Nailed it dude! Awesome video!
Congrats Steve, your product is very impressive! How many devs are working on it?
Wonderful insights. Congrats!
This cleared so much confusions! i'm gonna refer this video as one of the best Videos on AI build.I have an incredible passion for genearative AI. Your words are gem.
Steve, this is an incredible video. Thank you for sharing.
What a fantastic video, great overview and thinking.
I could also see a benefit to creating a "toolchain" with only certain parts using AI as the ability to override that AI if it makes a mistake. I think one of the biggest flaws of AI is that while it can produce incredible results, it is never "guraranteed" to work in the same way that a lot of normal algorithms can be guaranteed to work through rigorous logic. This is why AI is best suited for subjective applications where you really need to understand user's intent in a way not possible with normal code. By using AI only in specific points, it opens the way for a user to more easily tweak the results to their liking.
For example, in the website builder product, if a full stack AI produced a website which combined images in an awkward way the user didn't intend, the only recourse would be to either go in and fully edit the code to fix the issue, or to edit the FIGMA model and hope the AI interpreted it better the next time. But with the AI being localized to the specific selection of images, it would probably be possible to have a manual "merge elements into image" function which would specifically override the AI's decision about which elements combine to form an image, and then plug this result into the rest of the model which can create this image in the resulting code.
I've been saying for a while that while AI is really useful as a tool, it is not always the most accurate and any AI product needs to consider what AI is good at, what it might get wrong, and how best to implement it in a way that allows the user to check/fix the result of the AI if needed.
That's what im trying to tell everybody but people just seem too invested in it, from professors to students, to random business people, if AI takes over, there will be no Quality, especially in software
Suggestion on technologies that can integrate well on a toolchain ?
Excellent tips. Even more impressive product. Well done
Such a cool video, fresh, ty keep it up!
I appreciate this video, this is something I was thinking about
I have to say this is such an effective way to market your product. Offering legitimate advice and not just trying to beg people to be interested in it. Or maybe it’s not even your intention to market it because I don’t know if people who would watch these videos are in the UI / UX space but either way it comes across as genuine and helpful!
This is quite possibly one of the best videos on TH-cam today!
Much truth in this talk. Thank you Steve.
How do you make the excalidraw-looking visuals in your videos and sites?
To make sure that I understand your point, you mean to avoid only connecting with LLM such as open AI but to build a unique customized product by keep training the machine to reach the fine tuning level of product right?
Prieceless piece of notes! Thank you, man. These are exactly what I have thought about.
This video gave me a lot of advices which I couldn't get from others 😊
This is brilliant, and the model
Every single one of your videos are such high quality and always provide such unique and interesting insight. Awesome job Steve 👏
Really appreciate this content
Thanks very informative gave me a new perspective to solve problems
Thanks for sharing the journey. How do I link LLM. Use langchain?
I wish I saw this video a few months ago,
but thanks for the great work.
Great video -- also good demo!
Do you use OpenAI to generate the learning data?
This is gold - thank you!
This is absolutely amazing. And you're 💯 correct. I am building a product myself, and yes, I have learnt recently myself, that let alone custom trained models, even GOFAI (Good old-fashioned AI) or expert systems can often get a job done in a fraction of the time. So yes, I guess this video just gave me more confidence in my own approach. I'll absolutely try to build what I can from scratch before going to the API call. All the potential pitfalls you highlighted such as performance and cost are absolutely essential to consider for my case, since I am building for a 3rd world market and a real-time use case.
Thanks a lot. You're awesome to share this.
This was an the easiest subscribe of the year for me. Keep it coming my man. You rock.
How to get into building these specialized models? I am a backend dev, interested in AI but only in my spare time. How deep is the AI rabit hole?
Very impressive. Great video !
Question: Which programming language do you recommend to build AI powered web apps?
Amazing vid! Thanks Steve!
selfdriving systems are moving towards e2e, very similar to what you described they absolutely dont have...
This help so much. Thanks.
this was amazingly good and insight full thanks❤
Could u make a longer tutorial about this?
Requirements analysis is crucial in all systems, but in GenAI it seems obligatory to not pack the solution with high costs of model. Choosing good model and defining the usage from functionality point of view is most important.
Think of AI as an improved querying engine. It's *so* much improved that you could integrated much more closely to the overal system (unlike something like classical search engines).
I'n my own use case, I really really really miss the times when a search engine would JUST search for the words I type... things started to go astray when the default combiner became OR, not AND and by the time they've added all these "clever" algorithms to substitute my words for more "popular" choices and give me content that some pattern-matching engine has determined is related to my words... search engines have became worse than useless.
@@edgeeffect That's without even mentioning all the crap that comes in the first page which is basically the equivalent of those boring medium articles in programming but also for every other subject
Great video, a nice antidote to the ubiquitous invocation of the 'bitter lesson'!
Given that events that have occured in the last few days im now very weary of using OpenAI going further. With Sam Altman's departure the company seems like it could fail miserably now, and microsfot has just brought him in to build in house tech.