EP 480: Day Zero of AI: Why Generative AI Is Just the Start

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As someone that talks about generative AI literally every single day, and I've spent thousands of hours talking about it and learning from Fortune 100 leaders and in teaching enterprise companies how to use, you know, chat, GBT or Microsoft Copilot. To me, it feels like we're decades and decades into this generative AI wave, even though we're only a couple of years. And when you think about it and zoom out, we maybe haven't even hit the tip yet. We might still be at day zero of AI and that's what we're going to be talking about today. I'm very excited. So welcome to Everyday AI where we help you get past day zero, I guess. But we are your daily livestream podcast and free daily newsletter helping everyday people like you and me not just learn AI, but how we can all actually leverage it to grow our companies and to grow our careers. Because, yeah, development doesn't stop and neither do we. So that's why after you're done listening to this podcast, you need to go to our website@your everydayai.com there you can not only listen to like 500 episodes from some of the world's leading companies and leading individuals in AI, but you should also be signing up for today's daily newsletter and every day's daily newsletter that we send out where we recap the topic that we cover on the podcast and the live stream. But then we also keep you up to date with everything else that you need to know. So make sure you go do that. All right, I am excited to talk about day zero of AI, but before we do, let's first go over some of the AI news today. So first, Meta is developing its own in house AI chips. So according to reports from Reuters, Meta is making a significant push to reduce reliance on external suppliers like Nvidia by developing its own AI chips, a move that could reshape its infrastructure and advertising business. So according to those reports, Meta has successfully developed and begun small scale deployment of its own in house AI chips, or GPUs, collaborating with Taiwan TSMC for production. So these chips are already being used for inference tasks such as tailoring content to users, and Meta plans to use them for training AI models by 2026. So the company forecasts expenses of 114 billion to 119 billion in 2025, with up to 65 billion of that allocated for capital expenditures largely driven by AI infrastructure investments. So the move aligns meta with a broader trend among tech giants like Amazon and Google that have also been in Microsoft, that have also been developing their own, whether it's GPUs, TPUs or NPUs in house to speed up AI developments. All right, next, OpenAI has introduced new tools to help developers and companies just build smarter AI agents. So OpenAI has launched the Responses API and Agents SDK, new tools designed to help developers create create advanced AI agents that can handle tasks like web searches, document analysis and computer operations. So these tools aim to make AI more accessible and customizable, enabling businesses and developers to build agents tailored to specific industries and needs, from legal research to customer support. So the Responses API allows agents to pull real time information from the web, analyze large amounts of text, and even perform tasks on a user's computer. That's the big update here, right? A lot of these, some of these have already been available, but the computer use essentially, you know, OpenAI's operator, right. So now businesses and developers can, well, they can try their best to make something like operator or as good as operator, but at least now that technology is available via the API. So yeah, this is pretty big news and I think you're going to start seeing dozens of kind of computer using AI agent type startups pop up pretty soon, probably for certain niches and verticals. As you know, I don't think anyone's probably going to try to compete, you know, across the broad spectrum with open AI. All right, so for those stories and a lot more, make sure to go sign up for that daily newsletter@your everydayai.com. all right, enough chit chat on the AI news. Let's talk about the big picture. And that is we haven't started apparently. I mean, we haven't, right? Yeah, like I talk about AI every day, maybe too much, but the reality is we are not even crawling, probably. All right, enough of me chit chatting. I'm excited to bring on our guest for today. So livestream audience, please help me welcome to the show Ron Green, the CTO of Kung Fu AI. Ron, thank you so much for joining the Everyday AI Show.

Thank you for having me, Jordan.

All right, for those that don't know what is Kung Fu AI, aside from like one of the coolest company names ever we've had on the show.

Thank you, thank you. So we're, we're a strategy and engineering firm. We were like seven plus years old. All we do is AI from day one. We help companies adopt AI strategy. We Build custom AI solutions for them. Basically, anything you need to get started or build your AI roadmap or AI capabilities, we help companies with that.

Nice. So give me an example. Company comes to you. I mean, are they like, hey, we need to build off this new, you know, this new SDK from OpenAI. We need to make agents for our company, or they come to you with, you know, petabytes or whatever it's called of data, and they're like, help us use the AI. Like, like, what does it look like? And what's the end result?

Yeah, it's a little bit more the latter. We're basically solving really, really hard problems with, with custom AI solutions. So, you know, people will come to us and they'll say things like, you know, we're trying to automate trading. We want to build a system that can trade hundreds of millions of dollars automatically. Or we built a system that can predict the risk of breast cancer using just pure computer vision out to five years in adv. Superhuman level. That model's actually at the FDA right now for approval. Things like that.

Very cool, Very cool. So, yeah, make sure to check out the newsletter if you want to know more of that. But so, so, Ron, let's get to it. So it's your take that we're at day zero of AI. Why is that?

You know, it's my way of, of, of, of kind of waking people up and, and making them realize that we've barely gotten going. So I've been, I've been working in AI professionally since the 90s, and we've made enormous strides. It's unbelievable if you compare it to what, you know, compared to what we can do now to. Back then, you know, we would have said, oh my God, our dreams are coming true when I was in grad school. But it's also really, really clear that the slope, the, the velocity, the acceleration is so great that we've essentially done nothing. We'll look back in five years and we'll think that the capabilities we have now are cute. In the same way that, the same way we look back in 2020 with GPT2 and we're like, you know, that model that's interesting that it can almost do something useful, but I'm sure there's not really going to be that much advancement. We are about to Hockey stick is the point. And there's a bunch of reasons why.

Well, let's, let's start unpacking those. So, you know, as someone that's been in AI for three decades, and to say we're about to hockey stick. What causes you to. To believe that? Right? Because obviously there's been, you know, you mentioned GPT2, right? I remember using GPT3, you know, five years ago. And I was like, wow, this is pretty impressive. And you know, it's obviously the technology has grown exponentially since then, so why are we about to hit that hockey stick curve upward now?

There's a bunch of reasons. Let's just jump to the, jump to the chase. The big reason is that traditionally we've relied on something called supervised learning, right? I think everybody probably is familiar with this. You take a model and you teach it to do something that you don't know how to teach it explicitly. So just so everybody can have an image in their head. Imagine you're trying to train a model 10 years ago to recognize photos. We would not know how to do that with traditional software. We don't really know how our own brains do that. That's okay. We can take one of these big deep learning models and we can short enough examples and generalizes. The problem with that is it's. It's constrained by examples. You have to have a labeled example for every image. You have to know what the right answer is. And the model can generalize, but it really can't generalize beyond anything that we as humans can teach it to do. Kind of mimicking this process. Well, that's changed recently. Reinforcement learning, which incidentally the two main people behind that concept literally just won the Turing Award last week, which is like the highest honor in computer science for the work. Reinforcement learning with these new, much more capable language models have really kicked thing into high gear. And we now have empirical evidence that we can elicit reasoning behavior emergently from these systems. These systems. And technically we don't really need to go into any of the details. All that matters is we can take a strong language model and by having it learn certain types of verifiable domains, like learning how to program and learning how to code and teaching it to think analytically. We are now, and we see multiple Frontier labs have verified this over and over again. We're seeing these strong models develop reasoning capabilities emergently. And here's why that's so important. Humans, as far as we can tell, are the only creatures with sort of metacognition. We can think about thinking as soon as we have these models, which they can now introspect and think about thinking. You've got this sort of infinite recursion ability. We can think about thinking about thinking, and we can gleam all of the deep insights from that. So we're really going to be off to the races, I expect. It's not an exaggeration to say we'll have AGI within a year, probably within three at this point. And it's all based upon this new development.

Yeah, the, the AGI conversation is, is always fun, FYI. Make sure if, if you care about the whole AGI debate, make sure to tune in tomorrow. It's, it's going to be a good episode, FYI. But let's get back to this, this, this concept of, you know, supervised learning, reinforcement learning. Right. So for those people out there who probably unlike you and I, I like, I, you know, I, I read all these papers all the time. Right, I'm sure you do as well, Rod. But for everyone else, like what is that actually? Like what's the. For businesses? Right. When we talk about like reinforcement learning and, and models that can now reason and they can, you know, introspect. Right. Like, what's that tangibly mean for businesses?

It's going to be huge. You know, I think probably everybody's heard about agentic AI that's going to be really big. Why is that going to be big? Because these, we're going to have these AI models that we can give high level assignments to high level tasks and they're going to be able to go and navigate the messy world. And, and so like unlike traditional RPA where you know, maybe you're dealing with regular expressions and it's sort of whack a mole, you've got a million unending corner cases you've got to deal with. These models are literally going to be able to deal with situations that they've never seen before and reason through them in intelligent ways. So that's, that's one way. Now that whole agentic world is a little bit more distant than I think some people argue. I think it's going to be more of a 20, 26 thing than a 25 thing only because we're still kind of working out the kinks. But there are things like research agents, like deep research from OpenAI that are ready for prime time right now. And I use this all the time. I was using it this morning to go and analyze really, really, really compact, complex subjects and it came back with a multi thousand word analysis. I read through it. I think it probably saved me two days worth of work. And it's sor. High level, white collar, cognitively heavy work that we're going to see really being impacted in the short term.

Yeah. And, and I'LL have to put that in the, the show notes as well. We, we covered Deep Research a couple of times, but I think one thing like small like aside, I don't think anyone else is talking about the fact that Deep Research is technically using an O3 full version which is not out anywhere else. And it's actually using a mini ver. Right. It's using O3 mini as well. So it's actually like, you know, two different versions of O3 working together. Yeah, the, the research there is insane. I can't stop using the tool. You know what, what do you see as some of this biggest. Right. So we talked about how you see this impending hockey stick of growth and you know, something like Deep Research. But is there any other, you know, happening or development aside from the research itself? Right. That you' recently that you're like, okay, even as someone with, you know, three decades of experience, is there anything you've seen recently that has kind of shocked you in terms of AI's capabilities?

Yeah, you know, I, I don't exaggerate. I, I think I'm shocked on a weekly basis right now. I mean now some of this will be maybe less widely business applicable. But like what's happening in image generation and image synthesis and video and audio. Incredible. If, if you haven't checked that out, I, I go have some fun. You can go lose a weekend on YouTube seeing what's going on there. But on a sort of a more practical level, healthcare and science generally are about to be massively disrupted. I'll give you an example that just blows my mind. So I've, I've got a background in computational biology as well. And you know, we used to dream in the late 90s of being able to sequence entire genomes and we were, we would think, well what if we had the capability to combine like that sequencing technology with, with real artificial intelligence? Well, that's, that's here today. There are, you know, models out there like AlphaFold that have essentially solved one of the grand challenges of biology which is, you know, amino sequence, amino acid sequence to protein folding prediction. That is, I mean this is one of the most important accomplishments in the history of science and it's able, it's enabling amazing things like this. Yeah, there is a, like a Bioml group, it's a postdoc PhD led group, student group at the University of Texas at Austin and they held a hackathon about six months ago to develop novel proteins to fight cancer. And this was all done in one weekend open source modeling, I think 62 countries participated in. They have 20,000 sequences. They're going to have the final results in I think two months. So things have advanced so far that five years ago this was an open question whether this was even theoretically possible. And now you have hackathons on a weekend developing novel cancer therapies. I mean, it's just incredible progress.

Yeah, it's, it's almost wild to me to think about the disparity. Right. And even as you're talking about that, you know, one thing that was popping up in my mind is the, you know, Google co scientists, you know, very, very impressive, you know, early agentic research from Google that that's going to be I think extremely helpful in that field. But you know, one thing that just always baffles my mind, Ron, is is the disparity between where we're at. Right. Like you gave that example of the, you know, b. But then we have even like smart companies focusing so much time on just like using large language models to write like better LinkedIn posts. Right. And things like that. Right. Like are you ever baffled or maybe it's just me, but just at the disparity between of the capabilities and then what the average human is using this technology for. The average, even enterprise business. Sometimes I'm shocked.

It blows my mind all the time. You know, it's that old saying that the future is here, it's just not evenly distributed. I think a lot of people, they take a look at something and I guess it kind of makes sense. You take a look at something and you get a read on it and you say, okay, I understand where we're at and that may work. You know, back in the old days when things were moving at a slower pace, right now things are moving so fast. You know, if you were an expert in AI five years ago and you came back to work, you wouldn't even know where to start, right? So you know, I would encourage everybody to have their head on a swivel here because things are moving incredibly fast. And you know that old adage is it's not, it's not that AI is going to beat your business. It's people, you know, in businesses leveraging AI that are going to take your business.

I'm glad it's, you know, taking the business right versus taking the job. I've always like personally hated that like one to one case comparison because I'm like, you know, oh, like AI is not going to take your job. Someone that uses AI will. But I'm like, what if that person using AI is using an agentic swarm, right? Like essentially OpenAI just released an SDK and an API for agentic swarm. So it's like, okay, well that person could in theory maybe do the job of, you know, I don't know, 10, 50, 100 people. Right. Can you talk even just about the capabilities and what, you know, non technical people or you know, small businesses? Like, can you walk us through just what the capabilities they have? Because you know, I feel generally, you know, to have that top echelon of technology has only been afforded to the 1% of companies. Right. The full Fortune 500. Right. What, what does the everyday non technical person in business have at their fingertips?

If I would argue that the, you know, the premier sort of bleeding edge reasoning language models like Claude 3.7 or ChatGPT 4.5 with, with deep research, those models can be helpful to anybody. No, I don't, I don't care what your job is. If you're, if you're dealing with text or images or numbers or you're trying to think through problems or you're trying to understand data, those tools have a depth to them that most people, I think they just don't know how to use or they don't know how to explore. So you don't have to go crazy. You can leverage these consumer grade reasoning models right now and get an enormous benefit. I mean, it's very difficult for me to think of a business that couldn't benefit from some aspect of that.

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 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 Gen AI. 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 will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen AI. I'm wondering, is, is day zero shifting Right. Can, can companies be at a, at a negative? Right. Like, sometimes I'm flabbergasted. You know, big companies reach out to me and they're like, oh, you know, you know, we're just now getting licenses for, you know, co pilot or we're looking at licenses for, you know, chat, GPT Enterprise. And I'm like, you're a, a 20 billion dollar company. Like, like, why are you there? Is, is that day zero? Is it, is it moving?

You know, so that's a great question. I mean, honestly, I think we're, I think the technology, I think AI is a technology. Is that day zero? There are companies who are so far behind, they're like day negative one or day negative two. And I see this all the time. For example, when Copilot first came out, I know people in the AI industry that didn't believe it, thought it was bs, that it couldn't work. And I give presentations all the time. And I'll ask big developer organizations, raise your hand if you're using Copilot. And I would say, or something like Cursor, AI, some type of coding assistant. Invariably I get a split audience. It's like half are using it and the other half think it's just not worth their time. And they have no idea what they're missing because those tools are as powerful as the end user. Right. And so if they're not getting a lot of goodness out of it, it's almost invariably their lack of understanding at what they could use with the tool. Right. It's like if you gave somebody a hammer and they were like, well, I don't really see how this could be useful in my world. Building houses and it just boggles the. And that's just coding assistance. This is going to be applied more and more everywhere that the challenge is. I think part of the reason too though, a lot of companies and maybe a lot of people are a little confused is they look at their mobile phone devices and Siri is still as dumb as a bag of rocks. And you're like, I'm not so sure I believe this AI stuff is real. The problem is it's going to take a while for these really, really large corporations to integrate the capabilities that already exist right now. Like, it may be another year or two before Siri even becomes capable of doing the things that technically it could have, like two years ago.

Yeah, that's, that's a great point. Yeah. We've seen recent reporting say anywhere from 2026 to 2027 until we get the actual you know, AI, you know, series. So we'll see. You know, I want to follow up on this, this concept of coding in AI coding, which, you know, I know our audience isn't the most technical, but you know, I've, you know, in my 2025 prediction show I said the average person is going to be building their own applications by the end of the year. And then the anthropic CEO just yesterday, Dario Madi said that in three to six months, 90% of code is going to be AI and within 12 months it's going to be 100%. How do you see, you know, even with, with non technical people, but how is this concept of, of AI encoding really just gonna change how business gets done? I see a lot of different avenues, but I'd love to hear from, you know, someone that's been doing this for three decades.

You know, I, I probably think Zaria's estimates may be a little optimistic because any type of generative AI solution at this stage, even with reasoning models, does need some oversight because it can lose the thread or hallucinate or make mistakes. These systems are not omniscient. Within three to five years, I think the bulk of new generated code will be AI generated and validated by humans or some other type of system. But the important point is that this is essentially taking the most powerful invention humans ever have ever created, which is like the ability to program general purpose computers. Right? Let's just set the table really quickly. General purpose computers like the laptop here on my desk, they're Turing complete. They can literally do anything that we can write down the instructions for. Right. So they're kind of unbounded from a capability perspective. And once we're in the realm where the process of converting from our minds into computer instructions, once that step has been made, as trivial as just talking to a coding assistant and using just everyday plain English, that means application development and application customization and feature additions and feature enhancements. That is going to become dramatically less expensive. Dramatically less expensive. And it doesn't mean the software engineers are going to go away and certainly not in the short term. It means the amount that we can leverage and get ROI from new co development is going to skyrocket. I mean, it's almost hard to exaggerate how much that is going to change things. Things. There's that old adage that, you know, software is eating the world. Software is, software is becoming embedded in every aspect of our life. You know, we have like an operating system in our refrigerator. Well now we have the ability to Go build and modify and enhance these systems across the board with artificial intelligence streamlining that entire process.

You know, another hockey analogy, right, Aside from it, you know, growth hockey sticking upward, you know, they always say, oh, you know, don't, you know, you have to skate to where the puck's going, not where it is. My thought is no one knows where the puck's going. Right. If you skate where you think the puck is going, you're gonna miss the game. Right. The bus is left. You know, how can companies, you know, not just, you know, oh, how can they get ahead, how can they keep up? Because as someone that does this every day, I struggle. Right. And, and I, I agree with you. I think that, you know, we are going to see this, this hockey stick in the, in the next couple of months. How do businesses keep up?

So I, I have two pieces of advice that I typically give most businesses because you know, if you're, if you're an executive running a company, you know, you have a full plate. Becoming an expert in artificial intelligence isn't really an option. So it's two things. One is don't make the mistake that generative AI is all there is to AI. And the reason I say that is like I mentioned a second ago, generative solutions are incredibly powerful in the right circumstance. But I see executives frequently have the false belief that they can build some generous solution and just plug it in and they forget about how they actually use it. The fact that, that if you're working with Claude or Chat GPT or something, you're massage, you're giving it a prompt, you're getting an answer, you're correcting it, you're, you're having a back and forth and generative solutions right now without a human in the loop just really don't work in a production environment. So if you go put a bunch of money into a generous solution, you might be disappointed if you forget that really important fact. The other component is that domain specific AI is incredibly powerful. Now I'm talking about systems that don't have broad general capabilities, but they may have one or two or three very, very amazing superpowers. But that's all they can do, right? So you might have an AI system that can detect fraud at a superhuman level, or it can optimize product recommendations or inventory optimization or all these types of things. Those are really, really powerful bets where you don't have to worry about necessarily having a human loop. And then the last piece of advice I would give is your data, the data that you have that is proprietary to your business and that drives your business. If you can build AI solutions on top of that, then you're going to get the most ROI for a couple reasons. One is it's massive defense. You've got this data mode that, that, that is unique. Two, you can build capabilities to either cut cost, extend new functionality, have new predictive or perceptive capabilities based upon your own data. And that is a much, much better way to go into AI than building things or tools that are not based on your data. And that could overnight become a product somebody sells. And you, you just wasted this huge investment when you, if you just waste it waited here, you could, you could buy it, you know, as a service. So focus your investments on maximizing the utility that you can get out of your own data.

And you know, one more question as we wrap up here. If, if, you know, if we're at day zero and if generative AI is, is just the start, what's next? Right. I'm not asking you to look into your crystal ball and yeah, you know, come back in six months and say, how dare you not be able to predict the future, Ron. Right. But if generative AI is just the start, what's next?

I think the big next step. And you can have me back on the show in a bit and we'll talk about day one. It's going to be this, it's going to be. These systems have superhuman abilities that go beyond mimicking some existing human capability. Whether it's our ability to see or hear or something like that. It's. And we're pretty close to this. I think this is really best case a year, probably worst case five, median case, maybe two and a half. And what we're going to have is just as right now where there are reasoning models that can code at an elite level or solve math problems at an Olympic level. These models, we're going to start knocking down additional domains and we're going to have them be able to do novel scientific research, novel clinical diagnostics, not just like, hey, can you automate this thing? Humans already do, but they're going to discover novel insights, they're going to make recommendations, they're going to be able to be introspective on their own output and reason at a level that is so sophisticated, it is literally going to have to dumb down for us the explanation so that we can understand it it. And that is what I'm so excited about. And that's where we're going to be at day one, in my opinion.

Love to see it getting us all past day zero of AI and prepared for what's next. Ron, thank you so much for taking time out of your day to join the Everyday AI Show. We really appreciate it.

This is awesome. Thanks for having me.

All right, y'. All, As a reminder, that was a lot. This is one of those ones. I'm already going to say it. You might want to listen to this twice. You also might want to go to our website. Your everydayai.com sign up for the free daily newsletter. I'm going to have fun re listening to this one myself and writing down the most important takeaways for you to leverage what we just learned to grow your company and your career. Thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks y'.

All.

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