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Leveraging AI and LLMs: Transforming Ideas into Products
In today's rapidly evolving technological landscape, the accessibility and capabilities of artificial intelligence (AI) and large language models (LLMs) are democratizing innovation. For startups and established businesses alike, the path from a conceptual idea to a market-ready product is being profoundly reshaped. Here's why every decision-maker should take note and harness these advancements.
The Shifting Startup Paradigm
Traditionally, the world of startups was synonymous with technical expertise, often conjuring images of coders in hoodies hunched over their laptops. However, the arrival of generative AI has dismantled these barriers. Today, one need not be a seasoned software engineer to conceptualize, design, and deploy a product powered by AI. With platforms and tools at the forefront, even non-technical founders can now navigate this landscape effectively.
Harnessing the Power of AI Efficiently
For businesses aiming to innovate or revamp their operations, using AI effectively is crucial. It begins with a foundational understanding of both existing technologies and the rapidly evolving ecosystem. Engaging with tools and platforms that provide direct access to cutting-edge AI capabilities should be a priority. Doing so enables businesses not only to enhance internal efficiency but also to pioneer new services or products that were previously inconceivable.
Nurturing AI-Driven Entrepreneurship
Startups today are not just benefiting from AI capabilities but also redefining them. Businesses that adopt an AI-native approach unlock potential user experiences and business models that traditional methodologies might overlook. The efficiencies gained from deploying AI in operations, such as prompt generation, model training, and deployment, drastically reduce the costs associated with product development.
Sustainability in Innovation
An essential aspect of leveraging AI tools is understanding and sustaining core intellectual property. As startups become more equipped with AI, the differentiation will heavily rely on proprietary insights, market knowledge, and unique customer relationships. While the tools provide a broad playing field, the distinguishing factor will always hinge on the value proposition to the end-user.
The Roadmap to Adoption
For those at the helm of businesses, the journey involves a dual approach: a relentless drive for experimentation and a steadfast focus on delivering customer value. Decision-makers should encourage teams to immerse themselves in new technologies — from LLMs to automated coding assistance — to discover avenues for innovation. Balancing this experimental attitude with strategic business objectives ensures growth and relevance in a competitive market.
The Future Awaits
The evolution catalyzed by AI is but a glimpse of what the future holds. As businesses continue to adopt these transformative tools, the key takeaway is clear: embrace change, focus on core strengths, and commit to creating genuine value for customers. Whether refining an idea or scaling a solution, AI will be the engine propelling the next wave of successful enterprises.
By integrating insights and methodologies to effectively harness AI, businesses stand on the brink of unprecedented innovation opportunities. This new era invites all — from seasoned entrepreneurs to emerging startups — to reimagine what’s possible.
Topics Covered in This Episode
1. NVIDIA Inception Startup Program
2. Spotlight on Lightning AI
3. Rapid Changes in AI and Their Impact
4. Strategies for Startups in the AI Era
Podcast Transcript
Jordan Wilson [00:00:17]:
Generative AI has obviously changed how companies work, but I think it's also quickly changing the startup landscape. You know? Ten years ago, I think when you think of a a startup founder, you thought of someone maybe with a hoodie, you know, just coding and, like, you know, on their laptop for hours and hours, but it's not like that anymore. You don't necessarily have to be a a software engineer or have a background in machine learning to leverage AI to grow a startup or to turn an idea into a product. And that's what we're gonna be talking about today on everyday AI. What's going on y'all? My name is Jordan Wilson, and I'm the host. And this thing, it's for you. This is your daily livestream podcast and free daily newsletter, helping us all learn and leverage generative AI to grow your company and your career. So maybe it's your first time listening.
Jordan Wilson [00:01:10]:
Thank you for joining. If that sounds like you, what I just said, leveraging generative AI to grow your company and career, number one, you're in your right place. Number two, the next best place for you to be is our website, youreverydayai.com. Go there. Sign up for our free daily newsletter. Not only are we gonna be recapping today's shows and all the best insights from our guests, but you can go listen for free, like, 450 other episodes where I've interviewed, some of the world's brightest minds in AI, all for free. Whatever you need, it's there. Alright.
Jordan Wilson [00:01:41]:
So I'm excited today to talk about this concept of how we can just turn ideas into products and also talking a little bit about the NVIDIA inception program. But before we do, let's start off as we normally do by going over the AI news. So Google has rolled out its leading VO two AI video model to YouTube Shorts. So YouTube Shorts has upgraded its dream screen feature by integrating Google DeepMind's latest video generation model, VO two. So this enhancement allows content creators to generate video clips using text prompts, eliminating the need for a stand alone text to video model such as, you know, Sora, Runway, something like that. So Google has improved the dream screen performance, ensuring a faster and more seamless user experience. So, the feature allows users to specify styles, lenses, or cinematic effects, enhancing creative possibilities. So this isn't the full VO two, but, for a lot of people, they haven't really been able to access, this new video this new AI video tool.
Jordan Wilson [00:02:46]:
So there is a new way, to do that now. Next, Anthropic's newest AI model hasn't been released yet, but it is set to be a different kind of model. So Infropic is preparing to launch a new advanced AI model in the coming weeks weeks, which is expected to offer developers greater control. So the new model is described as a hybrid model that can switch between fast responses and deep reasoning, potentially being on the same level as maybe OpenAI's o3 minutei in their GPT four o at the same time with one model. We'll have to see. A notable feature, and this is according to reporting from the information, a notable feature is the introduction of a sliding scale allowing developers to control costs by adjusting the amount of reasoning power allocated to specific tasks. So Anthropic has been quietly working on this model, which aims to provide a balance between standard language model functions and deep reasoning capabilities. This news comes just hours after OpenAI CEO Sam Altman talked about a similar hybrid approach for the future development of ChatGPT as he posted on Twitter that the o series and the GPT series would essentially be merging in the future.
Jordan Wilson [00:04:00]:
Our our last piece of AI news, speaking of OpenAI, they have just released a prompting best practices guide for their o series model. So nothing groundbreaking here, but we'll be sharing it in our newsletter. It includes tips like keeping prompts simple and direct, avoiding chain of thought prompts since that's essentially what the o model does, using deliminators for, clarity, and to start with zero shot then move to few shot if needed. Alright. So we're gonna have a lot more on those stories and everything else that you need to be the smartest person in AI at your company. We're We're gonna be recapping it in our newsletter, so make sure you go sign up for that at youreverydayai.com. Alright. Enough chitchat y'all.
Jordan Wilson [00:04:39]:
I'm excited to talk not just about the NVIDIA inception startup program, but also highlighting one of their companies, Lightning AI, and also what Lightning AI can help anyone do and turning to the broader picture of today's conversation. So without further ado, please help me. We got a we got three people. We got three people y'all, so let's see if we can do this here. So, please help me welcome to the show. There we go. We have, Will Kofel, who is the director of the NVIDIA startup program, and William Falcon, the CEO and founder of Lightning AI. Will and William, thank you for joining the show.
Will Koffel [00:05:15]:
Pleasure. Hey Jordan.
William Falcon [00:05:16]:
Thanks for having us. Hopefully, the the names don't get confusing. You know?
Jordan Wilson [00:05:20]:
Yeah. Yeah. Well yeah. For for our livestream audience, probably a little easier. For our podcast audience, yeah. We'll we'll say Will at NVIDIA and William at, Lightning AI. But, let's start with you, Will. Can you tell us a little bit for those that aren't aware? What is the NVIDIA Inception startup program?
Will Koffel [00:05:36]:
Yeah. Thanks, Jordan. So, Inception is, a scaled program for every startup out there in the global startup ecosystem. Ecosystem. So we're looking for anybody from two developers in a garage all the way up to, you know, amazing companies like Lightning and the kind of super unicorns. And a big part of what Inception does is say, let's take you on a journey from, you know, towards your AI success, and we'll talk, I think, more about that today. But, but, really, that starts with how do we educate you on AI, on specifically what NVIDIA does, how can you take advantage of it. Obviously, this landscape is moving so quickly that, probably the biggest barrier is just trying to keep up.
Will Koffel [00:06:11]:
So a big part of what we're here to do is connect folks into all of the learning resources and all the myriad SDKs and evolving software stack that's coming out of NVIDIA. And then beyond that, as companies grow, start to find product market fit, start to get more investment, and start to adopt NVIDIA technologies. A big part of what we do is how can we provide more visibility? How can we, you know, get your folks on stage? How can we introduce you to customers? How can we introduce you to additional investors? So, really, we are here, kind of for for each stage of that. And then, obviously, NVIDIA is one big family in terms of supporting, you know, all of our customers, all of our partners. So, the other part of what we do is make sure that you're connected into the right resources around NVIDIA. So, anyway, without making it an infomercial, NVIDIA dot com slash startups is the right place to go for that. But, happy here to have have William from Lightning who's been, in our program for a while. Lightning has grown.
Will Koffel [00:06:58]:
They are kinda out of the nest and flying and doing amazing things. So, happy to tell their story today too.
Jordan Wilson [00:07:03]:
Yeah. So, William, let's just throw it straight over to you. Tell us a little bit about Lightning AI. You You know, I think a lot of people, if you are a developer, you probably know PyTorch Lightning, but tell us a little bit, about your story and what you all do.
William Falcon [00:07:16]:
Yeah. For sure. First of all, the session program is great, so fully, fully support what Will just said. So Lightning AI is a company behind PyTorch Lightning. That's kind of what we're best known for. And PyTorch Lightning is a deep learning framework that lets you train models at scale. So I'll give some examples of companies that use it and and how they've done it. Stability AI, for example, stable diffusion, all those models are trained using PyTorch Lightning.
William Falcon [00:07:41]:
LinkedIn recently published a paper on a 70,000,000,000 parameter MLM that they train with PyTorch Lightning as well. And then, you know, probably the first time we started our partnership with NVIDIA back in, like, 2019, '20 '20 was the software site called Nemo Today that's grown a lot. That's also built on total PyTorch Lightning. So a lot of those models train using that. So it's been great. And so as a company, what we have grown into now is really an end to end ML platform. And for most companies today, they're spending a lot of time going through, like, 20 steps to get a model or AI product into production. Right? You might have to, like, clean data, curate, pretrain models, fine tune, deploy, monitoring, this and that, and it's, like, 17 steps you have to do.
William Falcon [00:08:24]:
Most companies don't have ML engineers on staff that can actually do those steps, and so they're kinda stuck usually in, like, a notebook or something like that. So, ultimately, what we provide as a company is, like, the final step in that process, and those are through our AI hub. So if you go to lightning.ai/AIhub, you'll find those already prebuilt, and they're ready to deploy with that one click. It's an API that you get, and you don't have to be an AI expert to do that. You're gonna get things like, I don't know, r one, for example, six seventy fully, fully without distilling. That's gonna get deployed. Or things like fine tuning and deploying. I was I gave a talk at a bank a few days ago, and we fine tuned and deployed an r one model, 8,000,000,000 for about $12.
William Falcon [00:09:03]:
And they were shocked that they could do it for $12. Right? And I was like, it takes, like, an hour and a half. It's not that complicated. It was like no code. You click, upload, and it's done. Right? And that's pretty much for most companies. And then there's some companies that have ML people. The rest of the platform lets those people build those APIs ultimately.
William Falcon [00:09:20]:
So all those other steps from one to 10, you can do them on the platform as well.
Jordan Wilson [00:09:24]:
Yeah. William, can you talk a little bit just, you know, speaking of lightning, I mean, I think it's been lightning fast, just the landscape. Right? Because I remember even some of the earlier episodes of of everyday AI to do what you just talked about there would have been in extremely large endeavor. Right? You would've had to have a room full of engineers. You would've had, like, to have a a huge $6.07 figure budget, but now you can get these, you know, instances up almost instantly. How quickly has this whole AI space been changing? And, you know, what are you all doing to help keep pace with the development?
William Falcon [00:10:03]:
You know, ultimately, the I would say the first, first, first principle of how we built everything from scratch, even PyTorch Lightning, was with change in mind. Like, when I was a Facebook guy doing research in 2019, and even then, you could see that things were changing very quickly. This is pre ChatGPT. And so as a first principle, we said, look. Ultimately, we need to develop a platform that can, you know, dive in, pivot and adopt a new technology or develop something new for it. And that's what we did. And it took us a while. It took us about four years to actually get there.
William Falcon [00:10:30]:
But as a result, I'll give you, like, a real example. So r one came out, like, on a Wednesday or something like that. Right? By Thursday, we had it we had the whole thing already built, as API so you could deploy. And by Friday, we had a Fortune 100 pharma company with six seventy b in production, like, not not a prototype, not like a POC, like an actual production internally within, like, forty eight hours. Right? And for them, the the value is, like, as long as you have the Lightning platform, you're gonna be able to get the latest things very, very quickly. Right? So that's part of it, and we also have a massive community of over 3,000,000 developers that work with us to build a lot of these syncs as well.
Jordan Wilson [00:11:09]:
And and, hey, just as a quick reminder to our livestream audience, if you do have any questions, get them in now, for Will or William. So, Will, I wanna throw it over to you, because, you know, you've I'm sure you talk to, you know, dozens or hundreds of startups on an ongoing basis. Right? I wanna ask a similar question to you that I just asked, to William. How has, generative AI, large language models, how has it changed even the startup journey? Because, you know, like I said, to begin the show, I think a lot of times there's probably great ideas or great products that just never made it to market maybe five or ten years ago because they didn't have the right software engineering, developer, coders, etcetera. How has this changed from your point of view?
Will Koffel [00:11:56]:
Yeah. I think we see two patterns on that. One is one is the same thing that enterprises are tackling, which is how can a start up be more effective? There's a lot of buzz out there about, you know, the the one person unicorn. Like, what will it take in order to be so efficient with tools like lightning or with, you know, LLMs as coding assistance, which, of course, is a a space that's also, that's also evolving very, very rapidly. It was a great story this week, Jordan. I'm sure you saw about, someone who built WikiTalk, which was an infinitely scrollable, Wikipedia page as as a way to kill time and learn something. And they built it all in, like, an hour with Claude as their back end for, for code generation. So I think we're seeing these incredible stories come out about about efficiency.
Will Koffel [00:12:40]:
And, of course, that that transcends just the kinda gimmicky stuff all the way into true developer productivity of large teams. The the whole part of the software development life cycle we see being transformed by, by by kind of AI even as a human assist and then maybe much more agentic AI and where that starts to take over big parts like doing all of your security assessments in an ongoing way or managing your DevOps and deployment or fixing your routing tables automatically and monitoring them in your, in your IT infrastructure. So I think all of that falls into the category of how the startups improve their own efficiency. The really interesting part, though, is the the new startups that can come into being in the AI era that could never be there before. So these are AI native companies. So I think this is gonna be the year that we see true AI native companies really popping up because we've just reached that level of abstraction and tooling, things like lightning, all the LLMs, the coding assistance to, to get there. And what we're gonna see, in my opinion, there are probably two two groups there as well. You know, what is, businesses whose user experience could never have existed before LLMs and AI? So, hey.
Will Koffel [00:13:43]:
It's a whole new user experience. So what is the CRM of the future, for example? What is the, ecommerce platform of the future that will look back and say it couldn't exist? Just the way we look back now and say, you know, things like Uber could not have existed without without a mobile phone. There there's a there's a whole revolution of that, or HubSpot could never have existed without the cloud. And then the other side of that are new, our new unit economics, new business models that are they're gonna exist out of this. We're, as as we said, even the the the talk about the the price coming down, how how much you can do with how little in the AI era, we continue to see those prices plummet. And I think that's gonna be incredible for adoption, because it's gonna allow us to create this user experience deep personalization of products in a way that we've never seen before. So think those are two categories, both efficiency, but also just whole new things that we have to look forward to.
Jordan Wilson [00:14:31]:
So, Will, I I I do wanna follow-up on that. Right? I I think the, the the WikiTalk is a great example. Right? We'll share about that in the newsletter, but, essentially, I think this person built it in, like, three or four hours. Right? So I think the whole startup used to be like, oh, validate your idea in a couple of weeks, and then it's like, oh, you know, a weekend. Now it's like a couple of hours or in real time. Right? You have people building clones of actual large scale software like on a live stream. Right? So so with this, you know, the way I see it is is we're gonna be flooded maybe in a good or a bad way with even more and more products because it's easier and easier. So how could startups actually differentiate themselves when it is it it can be so easy now with AI?
Will Koffel [00:15:15]:
Well, I mean, this is we're talking idea to product here, I guess. And and the the gotcha on that is always product can mean different things. So I think right now, we're doing a lot of ideas to features. Ideas to products are beginning to happen. I think ideas to businesses is the place that start ups need to keep thinking about. At the end of the day, there still needs to be a value creation for your customers. So the idea that you can build faster, prototype faster, have novel, you know, user experiences still doesn't obviate the need to figure out how you're delivering value in into the ecosystem. So, you know, one of those differentiators there is gonna be, unfortunately, the same old, the the the same old things that we know about about startups, about understanding your market, about having good product management, about remaining agile, being able to pivot.
Will Koffel [00:15:57]:
All all of those things, I think, are gonna remain true in the AI era with a whole new set of tools. But, you know, there's certainly a trap that says, well, just because I can build it, it will be useful. I think we'll see a lot of stuff fall by the wayside or a lot of consolidation in in these markets. So I would say, you know, never forget it's about the customers. It's about product market fit, not just not just those sort of exciting products that are emerging.
Jordan Wilson [00:16:18]:
So, William, I I I wanna follow-up with you on this one because, you know, you gave an example of, you know, hey. New model comes out on a Wednesday. You know, within hours, you know, you can have a Fortune 100 company up and running on it. But I always like learning from companies and the way that they eat their own dog food. Right? So, you know, I'm curious. Right? So you're helping clients do this, but how is this, you know, the, the speed of of generative AI in large language models, how is this even helping you, you know, grow your current business or ideate on new products, products or service lines for your current clients?
William Falcon [00:16:56]:
Yeah. So, you know, we we ultimately develop a lot of developer tools, right, on the on the non AI hub side. We use AI internally for things like fraud detection because we run a very expensive GPUs, on diff different clouds. So we don't want, like, bad activity on that. These are APIs that are deployed and they're, like, monitoring workloads all the time. And those are powered by LLMs in production. Right? And, you know, it's funny because, like, one of those was built by, you know, kinda like, not really I mean, kinda mid level engineer, I guess, in about, I don't know, five or six hours. Right? Like a a like a a, what do you call it? A a production system monitoring for, you know, bad bad activity.
William Falcon [00:17:40]:
Right? Like, fraud detection. And it's the kind of things where, like, in even in our company, we'll have very junior engineers build very serious production things that, like, in in a different time could have been a stand alone company just to do that one thing. And for for, you know, for us, it's a few hours of work to do these kind of things. So it has changed a lot. We also use a lot of AI in customer service. So support or, you know, people on our open source repos when they're asking us questions. We have Discord communities. We have, communities on Reddit as well.
William Falcon [00:18:10]:
And, when people are asking things, like, these things help them out as well. And in documentation. Right? So because we have a lot of tools. So how can you find the most relevant things? And, and even for customers, we have ways of having these things help them write code for PyTorch, PyTorch Lightning, etcetera, etcetera, on the platform as well. And, and I think it's been hugely productive because now you you know, I'll give an example. We're only 40 people at the company. Lightning is only 40 people, including all of sales, engineering, and everything. And I think in a different world, we probably would have needed, like, 300 people to do what we're doing today.
William Falcon [00:18:42]:
Right?
Jordan Wilson [00:18:43]:
Yeah. That's that's it it it is wild to hear, you know, sometimes the, you know, these successful startups and you just think or assume. Yeah. They have to have, you know, hundreds of employees and, you know, I think it it really changes the the lean methodology. Right? Like, what you can accomplish with, so so few people. You you know, Will, I I I do have a question. Right? So, I'm sure you get pitched all the time and there's probably a lot of, you know, startup founders, listening to this and, you know, even businesses that are taking on this more startup mentality of, you know, finding, new verticals to serve with with, different products and services. But, you know, even now, because it is much easier to at least turn an idea into a product, doesn't mean it's a good product, doesn't mean it can be successful.
Jordan Wilson [00:19:25]:
But what are the differentiators in in today's, day and age of of AI and LLMs everywhere? What are the differentiators that can take it beyond a product and make it a successful product?
Will Koffel [00:19:37]:
Yeah. I think you touched on a few things there. Certainly, it it's all the old things that we know in terms of making sure you're answering the questions about why why this team, why this time, why this customer base. So I think a lot of those things are still there. What we are seeing is that, startups are getting farther with, with less money, certainly, at the application tier of of this. And I think investors are looking for more, fully fleshed out products. So the demo is becoming more important than the, than than the the pitch deck. And so, yeah, I think that's a big part of it.
Will Koffel [00:20:10]:
The other element is making sure that you know what your core IP is. And there, I think, it's always a trick when the landscape is moving quickly to say, what is it that I'm building versus buying? Sort of that build versus buy question comes up. So a lot of what we do at NVIDIA is remind startups of all of the frameworks that we have, and all of the tools that are out there and things like lightning and all these platforms so that you're not reinventing the things that are not your differentiator. So what are the what are the pillars that that are the structural, integrity for your for your house, so to speak, for your business? What are the things that if they go away, you you might as well not fund your business at all versus the things that are moving rapidly, like the latest models or the APIs that anyone can go and call? If you're basically just gluing those pieces together, I think it's a question about where your core IP is. So what I would say is stay very plugged into the ecosystem. Will you mention, you know, the community stuff? Yes. We also have Discord servers. Come to the NVIDIA registered developer program to see what the latest is coming out of there and, you know, and and all of our events and GTC coming up as NVIDIA's big events.
Will Koffel [00:21:12]:
So staying plugged into the ecosystem matters to the degree that you can find shortcuts. That allows you to more focus on your core. But I think there are gonna be a lot of startups who who lose a little bit of their way on that core, and investors are gonna wanna see what that is. What's the IP? What's the customer knowledge? What are the network effects maybe that you're bringing to the table? Because those are the things that can create kind of durability for the new startups.
Jordan Wilson [00:21:34]:
I think that's some some great advice there. So, William, I'll throw this one over to you, a question from our livestream audience. Sandra asking, as a technology advisory, can we leverage Lightning AI to create tech assessment tools with minimal coding expertise?
William Falcon [00:21:50]:
Yes. %. So that's that's a lot of what the AI Hub is. Right? So, if you I mean, they're they're kinda publicly there right now. So if you go to lightning.ai/aihebhub, you'll see them there. Those are no code solutions that have been prebuilt, and you can kinda, like, click and deploy things. And if you need specialized ones built, then that's where we can help as well. So if you're like, hey.
William Falcon [00:22:10]:
I want an agent that does x or an an expert that does whatever or, like, a way to fine tune this particular model, then we'll build that for you as well. Right? And we'll put it in the iHub. But I but I definitely, want to piggyback on this concept as well that you guys just chatted about, on, like, how do you differentiate and how do you build these products quickly? Like, I think, you know, to the to the audience here, I think this should be very encouraging because I think the next generation of great products are not gonna be be built by developers, actually. It's gonna be built by subject matter experts like lawyers or doctors who know their fields really, really well. And then they're gonna use no code tools or low code tools like Lightning and OpenAI and so on to basically build a lot of these products without needing to hire tons of engineers. Right? I think that's really where it's gonna come because they see the value. Engineers tend to wanna build tools usually, and that's not really I think at this stage, it's not really differentiated anymore like Will said. Right?
Jordan Wilson [00:23:04]:
Yeah. It's it's it's funny, William, you say that because, you know, I always think, like, if you're looking at a startup pitch deck, you know, and then there's the page with, like, you know, the advisors or those people that, you know, are are are there to provide guidance. I'm like, it's gonna be these people probably that are building the next generation of tools. Right?
William Falcon [00:23:20]:
%.
Jordan Wilson [00:23:20]:
Yeah. But but but, Will, I do have a question. So I think it was almost like a year ago, where, you know, your CEO, Jensen Wong, you know, came out and, you know, essentially said, hey. You know, I don't think kids necessarily need to learn to code. And, you know, at the time, I think a lot of people were taken aback by that comment. Right? Like, I wasn't. But here we are fast forward a year later, and maybe it makes a little bit more sense, right now now that you have this ability to essentially you can talk with your voice and and build something. Right? What's what's your take on this, you know, this thing this thing, like, vibe coding, right, where you just kind of go and you talk to an LLM and you can build your right like this WikiTalk example.
Jordan Wilson [00:23:59]:
What's your what's your thoughts on this, and is this kind of a viable way forward? Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on GenAI. Hey. This is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI.
Will Koffel [00:25:14]:
Oh, look. I think, I think it's certainly transformational to, to development. Everything that we're seeing around the code gen space. And I think the I think the the same thing's been true for for generations, which is maybe kids don't need to learn to code in the same way as the last generation. They still need to learn to think. And what this whole revolution is doing is kind of separating out some of the mechanics, of, of what we do from from the creativity. So the idea to kind of curate your ideas and your focus is still gonna be top of mind. And and and I think it's gonna accelerate, I think, the creativity of kids.
Will Koffel [00:25:50]:
I mean, this doesn't just touch coding, by the way. It touches things like, like, Jordan has a has has a new baby. Congratulations, Jordan. And, and, you know, and I think this idea that you're gonna have a a storybook, a picture book where a kid's gonna say what they want to happen on the next page, turn that page, and have the story rendered out for them immediately from their own imagination is such an incredibly powerful enabling tool. And we're gonna see the equivalent of that in the coding in the business space too where people are able to play with those ideas and prototype them more quickly. But but that doesn't account for taste. It doesn't account for understanding your product market fit. So, you know, I think all the same stuff is gonna happen when it comes to to to building tools.
Will Koffel [00:26:31]:
It's just gonna be easier and and faster. So I I, you know, I certainly as a a long time engineer myself, I've been a serial founder and and have a computer science background. I I feel enthusiastic, not intimidated by by the idea that that a computer can code for me because that was never the fun part of the job.
Jordan Wilson [00:26:48]:
Yeah. No. It's it's it is weird. Right? It's it's weird now to see, you know, what large language models are are capable of that a lot of humans used to hold that close to their chest and be like, this is my special thing. And it's like, oh, well, large language models are actually fantastic at this now. Another good question here for you, William, someone asking about Lightning AI. So, someone Lisa's asking, can you provide examples of the kinds of things that you can imagine maybe doctors or lawyers, might create with Lightning AI?
William Falcon [00:27:19]:
Yeah. Okay. So I'm gonna answer that in a general way and and specific as well. But I I wanna teach you guys the principles of how to think about this. Right? So, look at look at your work. Anything that you're doing that is repetitive or that you have some kind of more junior analyst doing that's repetitive, or you're you're finding yourself giving a lot of feedback, like, lawyers have this problem a lot. Right? Like, you'll have a senior partner who has these associates that ask the same questions all the time, and they're like, look. Kind of the same answer no matter which associate asks.
William Falcon [00:27:50]:
Right? So the so you might want to, like, upload emails from that partner and their responses. And then when an associate asks to, like, an expert who's that partner, it'll answer with that partner's context in mind. Right? So so you wanna be looking at these repetitive tasks that are not really, I don't know, it's it's a bit of, like, encoding your own expertise, really, is I think how you should think about it, into into a document that then other people can tap can tap into. I think that that's one example for lawyers. For doctors, it's gonna be a lot of the same thing. Like, do you find yourself kind of going through the same answers because someone said I have this and this and that? Like, we we fine tune an example model. You know, I'm not a doctor, so I don't know how good it is, but it's for diagnosis. Right? So we we, my cofounder, my, CTO, Luca, came from the medical, imaging world.
William Falcon [00:28:41]:
Right? And he basically, looked at a data set of, I guess, patients, like, reports on, like, what the diagnosis should be. And, and then we find in this model. And you can ask this thing, like, you know, I'm a man of this age, and I have these symptoms. Like, what is the thing? And it gives you, like, options. Right? Like, I don't know how valid they are, but that that's the kind of work where you want to have a a a model give you a pass at it, and then a real lawyer or doctor verify what that is. Right? So I think it's for a while, we should still have human in the loop for a lot of this, but, you can do that v one. Like, basically, convert yourselves to editors instead of creators. Have something else do the creation, and you are the editor for that creation now instead of you creating everything yourself.
William Falcon [00:29:25]:
Those are the principles that I would think about.
Jordan Wilson [00:29:28]:
I think that's such such great advice. Right? Yeah. Finding where the the the the human's role is in the human in the loop conversation because it it changes. Right? So it Will, you you know, I'm curious. You know, when we talk about competition, you know, especially, as tools like Lightning AI, you know, do make it easier to turn an idea into a product, how can startups or even businesses, you know, remain competitive? Because it seems like, you know, you can have a a clone or a competitor almost out of nowhere. So so what's your your best advice, you know, to address, you know, how quickly competition can move now because of generative AI?
Will Koffel [00:30:14]:
Yeah. I think it's gonna be interesting to see how how that evolves. I think we've always, for lucrative markets, have the ability to clone. Maybe we can clone or, you know, or fast follow is probably the more common case here. You know, the companies can fast follow on markets that are that are gonna be lucrative where the where the unit economics play out. So I'm not sure that it's fundamentally different, although we may find, as you say, that they pop up out of the blue more. At the end of the day, that's where it comes back to what what are your customer relationships. It's the kind of integrity of, of your team, you know, the trust and reliability there.
Will Koffel [00:30:46]:
I think the real risk is if you've got, if you've got the ability to create kinda overnight products, then you might end up having kinda fly by night businesses. And and at the end of the day, every partner out there, everyone who's taking you know, who who's spending every, every, enterprise who's paying a vendor for a solution is gonna wanna know that there's support behind that, that that's gonna grow with them, that their feedback is gonna be heard. So a lot of the a lot of the market facing skills are probably gonna become even more important. But that's not to undermine the idea that every technology invention has allowed us to get more sophisticated in the products themselves. So I think on the one hand, we can imagine today, wow. We can build today's products a lot easier than than we built them in the past. But I think we should start thinking about tomorrow's products. So it might be that we have all these amazing smart people sitting around who have now, this sort of these incredible tools at their disposal.
Will Koffel [00:31:38]:
And so it's not just that they're gonna create the same things faster. It's they're gonna create more in the same amount of time. So, you know, I think building building more features, building better user experiences, building more interactivity, more personalization, I think these things that didn't make sense before from a cost perspective are gonna start to be enabled in in more of our products. So I think you're gonna get both sides of it. But I think from a from a competition standpoint, it's it's anchoring back on, back on moving faster, creating more value for the customers. Those are the those are the things we've been doing forever, unfortunately. But but AI tools will make them make them different, but certainly not go away.
William Falcon [00:32:14]:
Mhmm. That's I think that's alright.
Jordan Wilson [00:32:16]:
That's great advice for, those in the position of trying to learn how to build and, you know, maybe separate themselves from the competition. But, you know, we've covered a lot in today's episode. So today's newsletter is gonna be a good one, FYI. But I'm gonna ask both of you as as we wrap up here. So I'll start with you, William. So what is your best piece of advice maybe for, you know, companies that are looking to leverage large language models? You know, what's your advice to them on how they can, you know, better, or more confidently turn new products, new services from ideas into actually, you know, economically viable, parts of their business?
William Falcon [00:32:59]:
Yeah. Great great question. I think it all starts with the the data. Right? So I think a lot of it's gonna be text nowadays that goes into these models. So I would write all your memos and emails and internal documentation, all your wikis with the writing. Like, don't write it for you or other humans. Write it for LLMs. Because you are going to feed all that stuff to an LLM at some point.
William Falcon [00:33:24]:
Right? So you should really be thinking about when you describe something, describe it so that you're going more explicit in instructions or the context so that a model can then later use it to train. Right? And then, I I think in the long term, the LLM is a tool that we will not really be thinking about. Like, you don't think about the engine in your your car when you drive it today. You don't think about physics when you drive a car. You don't think about how the gas gets to the engine or spark plugs or any of this stuff. Right? There are some dedicated experts who know how to do that, and they're called mechanics, and they're very far and few. But the majority of you don't think about it. And I think that's how your products are gonna end up being.
William Falcon [00:34:00]:
Right? And I think a lot of, like, what we're trying to do is make that the reality today. Right? So, my advice at that you know, kind of the next point would be, like, once you have, if you already have that documentation, you have those emails, you have all the things written, come to a product like lightning or call us, and we will help you build exactly what you're looking for very, very quickly within a few weeks. Right? But we need that that we need you to be able to curate that stuff because that's where your knowledge is. We're not experts at that. Right?
Jordan Wilson [00:34:26]:
So, Will, I'll ask you the same question. In an AI everywhere world, what's your best advice for those looking to turn ideas into products?
Will Koffel [00:34:37]:
I think right now, my best advice is to to continue to experiment and to play with the actual tools. I think it's very easy to get caught up in the noise of, you know, reading about all these things, kind of learning about them in the abstract. What we find is, as with many entrepreneurial, motions, that bias to action is gonna serve, is gonna serve entrepreneurs well. So the more people can play with things, can get out there, can trade try things like AI Hub from, you know, from Lightning or, visit, ai.NVIDIA.com. It has a playground where people can go in and try all the latest models from us, all the latest open source like LAMA, Stable Diffusion, DeepSeek r one. So getting and playing and seeing, hey. How do I how do I learn that? That, I think, to William's point about learning to speak language in the era of artificial intelligence, which has become our kind of Rosetta Stone of this. We've we've picked we've picked our own languages as a way to interact, but I think we still need to learn how to speak in a way that the AI understands us.
Will Koffel [00:35:37]:
And getting familiar with that is gonna give a leg up to to entrepreneurs and, of course, to end users alike. So I think the biggest thing there is to continue to experiment. And the caveat on that because because it's always a a double edged sword is that at the end of the day where the rubber meets the road is ultimately creating value out of your own product. So I think there is a balance. Some people can get lost so much in the experimentation that they forget to just hunker down and solve the problem they're solving. So at at some point, you kind of you go to battle with the with the weapons that you have at your disposal and you go and and create value out there. But that balance of continually experimenting to find the opportunities for innovation, while while just while just buckling down and doing the hard work of starting a business, I think that balance is gonna be the trick for the next few years.
Jordan Wilson [00:36:17]:
Speaking of, rubber hitting the road, I think both of you helped, create a very good road map, for so many of our audience out there, that are going through these challenges. So, Will and William, thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it.
William Falcon [00:36:34]:
Thanks so much for having us.
Jordan Wilson [00:36:36]:
Alright. And, hey, as a reminder to y'all, that was a lot. So many great pieces of advice from multiple perspectives. I hope you enjoyed today's show. If you did, you're going to enjoy our write up. So if you haven't already, please go to youreverydayai.com. Sign up for the free daily newsletter. And if you are new here, make sure to subscribe on the podcast.
Jordan Wilson [00:36:57]:
You you know, reach out. Let us know what you wanna hear more of. So thank you for tuning in. We'll see you back tomorrow and every day for more everyday AI. Thanks, y'all.
AI [00:37:09]:
And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayAI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time.