I’m excited to share this conversation with Faizaan Charania. Faizaan is an AI product lead at LinkedIn. During this conversation, Faizzan discussed the potential of Generative AI and its applications, the importance of keeping GenAI solutions simple, and how to think about trust, transparency, and managing costs as a product manager working in Gen AI.

Links

Faizaan On LinkedIn

Transcript

[00:00:00 ] Faizaan Charania: I love the analogy with cloud because cloud can make experimentation so easy. And you're just like trying to set up something new. Test it out. See, see if it works. Will I get product market fit? What are my users thinking about this feature? All of these things are also possible with GenAI. So for any PM who's thinking about GenAI, my recommendation would be test it out.  

[00:00:24 ] Hima: I'm, Himakara Pieris. You're listening to smart products. A show where we, recognize, celebrate and learn from industry leaders who are solving real world problems. Using AI.

[00:00:34 ]  

[00:00:35 ] Himakara Pieris: My guest today is Faizan Charania. Faizan, welcome to the show.  

[00:00:40 ] Faizaan Charania: Thank you so much for inviting me, Hima.  

[00:00:43 ] Himakara Pieris: To start things off, could you tell us a bit about your background  

[00:00:46 ] Faizaan Charania: Yes. I am a product manager at LinkedIn. My main focus is around machine learning and artificial intelligence.

[00:00:53 ] Faizaan Charania: And obviously these days I've been looking into gen AI as well. I've been in the machine [00:01:00 ] learning field for around Eight, over eight years now started on the research side, worked with startups, uh, was a machine learning engineer for a bit. And then I switched to product management.  

[00:01:12 ] Himakara Pieris: There is a lot of attention on generative AI at the moment. Could you tell me a bit about the way you see it? What is generative AI and how it's different from all the various other types of AI that we have seen so far?

[00:01:24 ] Faizaan Charania: Yeah, definitely. There is so much hype around gen AI. Uh, one thing, uh, one code that I've heard multiple times is. Uh, this is like the iPhone moment or this is the desktop to mobile moment of technology again. To answer your second question around, uh, how is it different from all other kinds of AI?

[00:01:46 ] Faizaan Charania: Because it's like so many things that we can qualify as AI, right? So a simple explanation that I try to go with is. Differentiate these two types of AIs, analytical AI and generative AI. [00:02:00 ] So analytical AI is where you, where you have some specific features or data points or like historical input, and you're trying to make one single decision based on that.

[00:02:12 ] Faizaan Charania: So the decision can be, Hey, is this email spam or not spam, spam classifiers? It can be a ranking decision. So say you log into Facebook or Instagram or like any of these applications and what post should appear first? What should be first? What should be second? What should be third? And this is based on the text in the post, the images.

[00:02:35 ] Faizaan Charania: It's based on what you like, what you don't like. So this is like a ranking problem. So ranking, decision making, all of these are a part of analytical AI and generative AI. As the name says, it's about taking, uh, generating new content. So if it's about post completion and everyone has heard about child GPD, so I'll just like [00:03:00 ] use that as one of the examples.

[00:03:02 ] Faizaan Charania: Like, Hey, I asked you a question and give me a response in natural language format. So natural language generation is generative AI generating new images, images that did not exist before is generative AI. So like even for images, if you were to classify an image, Hey, is this. Safe for children or not safe for children, that's analytical, but if you want to generate a cartoon image, that's generative.

[00:03:30 ] Himakara Pieris: From an overall landscape standpoint. So we have a ton of startups that are out there and then there are a couple of, in a way, key gatekeepers, Microsoft slash open AI. Um, I would say one of them, and then there is an emerging, emerging rivalry with, with Google, um, or refresh rivalry with Google on this front.

[00:03:53 ] Himakara Pieris: And then there are also chip makers. How do you. So if a map out this landscape, [00:04:00 ]  

[00:04:00 ] Faizaan Charania: yeah, so, uh, when you're thinking about landscape, yes, Google and Microsoft are big players, but then there's like so many more important players over there. So if you're just thinking about the flow of generative AI at the base layer, you will have the infrastructure companies, these chip companies, and they are the ones who actually make gen AI possible.

[00:04:25 ] Faizaan Charania: So that's one thing then at the top level, you will have applications that are using generative AI. And in the middle, you would find all of these other players who are building new features and new utilities to even make gen AI, um, efficient. So to give you one example for prompt engineering, there's new companies that are just focused on prompt engineering, making prompt engineering easy.

[00:04:52 ] Faizaan Charania: Versioning of it, iteration, structures of it. Um, there's a prompt engineering [00:05:00 ] marketplace now. So people can sell prompts and people can buy prompts. So, I, yes, Microsoft and Google are the popular ones because they're like big players so there's like more Um, media limelight around them, but I think they're, they're just like one of the initial pioneers and there's just so many players and there's so much scope for everyone to be a part of this.

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[00:05:24 ] Himakara Pieris: So I think what we're talking about is there is the foundational layer, right? Which Microsoft and Google's of the world are going to provide similarly to how they provide cloud computing today. And there's going to be a huge ecosystem that is getting built on top of it. And prompt engineering sounds like.

[00:05:42 ] Himakara Pieris: One big part of it prompt prompt engineering and everything that's that goes around prompt engineering Are there any other ecosystem participants at that layer  

[00:05:54 ] Himakara Pieris: in your view  

[00:05:56 ] Faizaan Charania: In the initial days The market is going to evolve a lot [00:06:00 ] So when these new models were launched and again, I'm talking about November and December you You might have seen, um, a large list of startups that just like came about.

[00:06:13 ] Faizaan Charania: So those are the ones who are early adopters and who are just making these things, uh, making like new applications possible. I think that's just the spur and that's the wide net that we are casting. But as time progresses, this is going to become business as usual. Gen AI won't be exciting anymore. Then the problems to solve are, hey.

[00:06:34 ] Faizaan Charania: How do I scale this? How is it going to be efficient? How do I do it for cheaper? And there are many different players who are playing in the infrastructure side of this. There are many new startups. I, there's this one startup that I. Sort of from, I can't remember their name, but, um, they've been working on making Gen AI more efficient for like three years now.

[00:06:59 ] Faizaan Charania: So Gen AI for the [00:07:00 ] public, it's, it seems like a new word and all of us are talking about it right now, but the early seeds were sown in 2017. And actually even before that, everyone has been building on the top of giants that came before them. But yeah, the concept has been around for a while and there are new marketplaces.

[00:07:19 ] Faizaan Charania: There are no new ecosystem players that are just going to solidify even more as time passes.  

[00:07:25 ] Himakara Pieris: Let's say you are a product manager, , for a product that exists in the market today. Where do you see opportunities and threats and challenges, , someone should look out for, , as a PM?

[00:07:39 ] Faizaan Charania: . My approach to Gen AI is to just think of it as a tool as it is. I've been doing this for like AI for a while and now Gen AI is just a flavor of it, right? So think of it as a tool and see how this tool can help me or my customers.

[00:07:54 ] Faizaan Charania: Solve their, solve for their opportunities or solve the challenges that they're facing, more easily. [00:08:00 ] And that is the core of how we should approach all kinds of product solutions. And then see where can Gen AI come in? How can we solve problems using Gen AI? Is there some flow or some funnel that my user is going through right now?

[00:08:15 ] Faizaan Charania: Where's the friction? Can Gen AI solve that? Can Gen AI make something possible which would make my users happy? But it was too difficult to do in the past. So there are many ways to think about this. The core of all of this should be the jobs to be done, the user needs, and then see where the unique capabilities of Gen AI are going to be useful for them.

[00:08:41 ] Himakara Pieris: What I'm seeing is that you can use. Generative AI for summarization, , expansion, style translation, I think I can put. Graphic stuff for diffusion into into one of those three buckets as well.

[00:08:56 ] Himakara Pieris: Am I missing something here?  

[00:08:58 ] Faizaan Charania: Summarization, [00:09:00 ] expansion, style translation. There's obviously all kinds of like generation. When you say style transformation, this could be just text style transformation.

[00:09:09 ] Himakara Pieris: It could be anything from turning Drake's voice into JC's voice. I think I see all those as some