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Unpacking Day Zero of AI: Why Generative AI Is Just the Beginning
In a rapidly evolving technological landscape, businesses stand on the precipice of a transformative era. Generative AI has gripped industries with its profound capabilities, yet experts assert we are merely at day zero. This isn't just another tech wave; it's an overhaul of how we perceive and leverage artificial intelligence. Dive into the pivotal insights gathered from a recent podcast episode that explores this nascent stage of AI and what it means for forward-thinking businesses.
The Illusion of Progress: Are We Really at Day Zero?
Despite the thousands of hours spent by AI experts learning and teaching AI technologies like chat GBT and Microsoft Copilot, there's an emergent realization: the journey of AI is only beginning. Though generative AI appears mature with its significant strides, we might be standing at the very genesis of AI's potential. As businesses, understanding that this might just be the early stages is crucial. This perspective shift equips decision-makers to pivot strategies, ensuring they are not left merely catching up but leading from the front.
Why Generative AI Feels Like Just a Teaser
Generative AI has showcased its utility, but the advancements don't stop here. The influx of interest is mirrored in the investments by technology giants like Meta, which are developing in-house AI chips to reduce reliance on external suppliers. Such moves underline a broader trend where businesses are creating proprietary solutions, aiming to speed up AI developments. This strategic pivot highlights the importance for businesses to continually innovate internally, aligning with giants who foresee the future of AI deeply embedded within their operational frameworks.
Reinforcement Learning: The Catalyst for a New Era
Deep within the podcast, one fine point emerges: the shift from supervised learning to reinforcement learning. This transition is not merely evolutionary but is set to be revolutionary. Supervised learning demanded vast amounts of labeled data, limiting the scale and speed of AI's learning capacity. Now, with reinforcement learning, models can autonomously improve through trial and error, much like human learning. For businesses, this means AI systems capable of reasoning, paving the way for advanced capabilities that move beyond human limitations.
The Business Imperative: Harnessing AI’s Potential Now
Businesses face an imperative: leverage this burgeoning technology or risk obsolescence. With the emergence of tools like OpenAI's responses API and agents SDK, the path to creating advanced AI agents is clearer than ever. These tools allow customization specifically tailored to industry needs, whether for legal research, customer support, or complex document analysis. In harnessing such flexibility, businesses can unlock efficiencies and innovative solutions unique to their domain.
Future-Proofing Your Business: Steps to Take Now
Acknowledging that we are at day zero of AI is an opportunity wrapped in urgency. Businesses must:
Invest in Proprietary Data Use: Allocate resources to building AI solutions atop proprietary data, creating defensible technological moats.
Explore Domain-Specific AI: Beyond generative AI, delve into specialized AI systems that offer superhuman capabilities in niche areas—whether it’s predicting fraud or optimizing supply chains.
Empower Teams with AI Tools: Equip teams with cutting-edge AI tools available today, ensuring that they are not merely passive users but active shapers of AI's role in your business.
Topics Covered in This Episode
Topics Covered in This Episode:
- Generative AI's current phase
- Meta's in-house AI chips development
- OpenAI's new developer tools
- Day zero of AI and future prospects
- Reinforcement learning advancements
- Emergent reasoning capabilities in AI
- Business implications of AI advancements
- AI in healthcare and science
Podcast Transcript
Jordan Wilson [00:00:16]:
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 and, 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 gonna 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.
Jordan Wilson [00:01:12]:
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 at youreverydayai.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 livestream. But then we also keep you up to date with everything else that you need to know, so make sure you go do that. Alright. I am excited to talk about day zero of AI, but before we do, let's first go over some of the AI news. 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.
Jordan Wilson [00:02:14]:
So according to those reports, Meta has successfully developed and begun small scale deployments of its own in house AI chips or GPUs, collaborating with Taiwan's 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 forecast expenses of a hundred and 14,000,000,000 to a hundred and 19,000,000,000 in 2025 with up to 65,000,000,000 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 and Microsoft that have also been developing their own, whether it's GPUs, TPUs, or NPUs, in house to speed up AI developments. Alright. 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 advanced AI agents that can handle tasks like web searches, document analysis, and computer operations.
Jordan Wilson [00:03:31]:
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 gonna start seeing, dozens of kind of computer using, AI agent type startups pop up pretty soon. Probably for, certain niches in verticals as, you know, I don't think anyone's probably gonna try to compete, you know, across the broad spectrum with OpenAI. Alright.
Jordan Wilson [00:04:33]:
So for those stories and a lot more, make sure to go sign up for that daily newsletter at youreverydayAI.com. Alright. Enough chit chat on the AI news. Let's talk about the big picture and that is we haven't started.
Ron Green [00:04:46]:
Apparently
Jordan Wilson [00:04:46]:
I I mean, we haven't. Right? Yeah. Like, I talk about AI every day, maybe too much, but the reality is is we are not even crawling probably. Alright. 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.
Ron Green [00:05:09]:
Thank you for having me, Jordan. Alright.
Jordan Wilson [00:05:11]:
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.
Ron Green [00:05:18]:
Thank you. Thank you. So we're a we're a strategy and engineering firm. We we we're, 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 road map or AI capabilities, we help companies with that.
Jordan Wilson [00:05:39]:
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?
Ron Green [00:05:56]:
Yeah. It's a little bit more the latter. We're we're 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 we're trying to automate trading. We wanna 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 advance at, like, a superhuman level. That model's actually at the FDA right now for approval. Things like that.
Jordan Wilson [00:06:25]:
Very cool. Very cool. So, yeah, make sure to check out the newsletter if you wanna 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?
Ron Green [00:06:36]:
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 nineties, and we've made enormous strides. It's unbelievable. If you compare it to what, you know, compare it 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 we'll the same way we look back in 2020 with GPT two, 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 gonna be that much advancement.
Ron Green [00:07:25]:
We're we are about to hockey stick is the point, and there's a bunch of reasons why. Mhmm.
Jordan Wilson [00:07:29]:
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 to believe that? Right? Because, obviously, there's been, you know, you mentioned GPT two. Right? I remember using GPT three, you know, about five years ago, and I was like, wow. This is pretty impressive. And, you know, it's obviously, the the technology has grown exponentially since then. So why are we about to hit that hockey stick curve upward now?
Ron Green [00:08:01]:
There's a bunch of reasons. The let's just jump to that jump to the chase. The big reason is is that traditionally, we've 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 train trying to train a model, ten years ago to recognize photos. We would not know how to do that with traditional software.
Ron Green [00:08:31]:
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 show it 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, kinda mimicking this process. Well, that's changed recently.
Ron Green [00:09:02]:
Reinforcement learning, which incidentally, the some of the the two main people behind that concept literally just won the tour 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. So these systems and, technically, we don't really need to go into the 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 with 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're you've got this sort of infinite recursion ability.
Ron Green [00:10:24]:
We can think about thinking about thinking, and we can gleam all of the deep insights from that. So we're really gonna 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.
Jordan Wilson [00:10:43]:
Yeah. The the the AGI conversation is is always fun. FYI, make sure, if if if you care about the whole AGI debate, make sure to tune in, tomorrow. It's it's gonna 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, 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, Ron. But for everyone else, like, what does that actually like, what's the tangible benefit for for businesses? Right? When we talk about, like, reinforcement learning and and models that can now, reason and they can, you know, introspect.
Jordan Wilson [00:11:23]:
Right? Like, what's that tangibly mean for businesses?
Ron Green [00:11:27]:
It's it's gonna be huge. You know, I I think probably everybody's heard about agentic AI. That's gonna be really big. Why is that gonna be big? Because these it we're gonna have these AI models that we can give high level assignments to, high level tasks, and they're gonna be able to go and navigate the messy world. 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 gotta 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.
Ron Green [00:12:12]:
I think it's gonna be more of a twenty twenty six thing than a 25 thing, only because we're still kinda 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 I use this all 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 sort of that high level, white collar cognitively heavy work that we're gonna see really be impacted in the short term.
Jordan Wilson [00:12:52]:
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 o three full version, which is not out anywhere else. And it's actually using a mini right? It's using o three mini as well. So it's actually like, you know, two different versions of o three working together. Yeah. The the the research there is insane. Insane.
Jordan Wilson [00:13:19]:
I can't stop using the tool. Go ahead. You know, what what do you see as as some of this biggest. Right? So we talked about how you see this impending hockey stick of growth and, you you know, something like deep research. But is there any other, you know, happening or developments aside from the research itself, right, that you've seen 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?
Ron Green [00:13:51]:
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 you if 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, health care and science generally are about to be massively disrupted. I'll give you an example that just blows my mind.
Ron Green [00:14:26]:
So I've I've got a background in computational biology as well, and, you know, we used to dream in the late nineties of being able to sequence entire, genomes, and we would 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.
Jordan Wilson [00:15:10]:
Yeah. And then
Ron Green [00:15:11]:
There is a, like, a BioML, group. It's a post doc, 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 gonna 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, novel, cancer therapies.
Ron Green [00:15:56]:
I mean, it's just incredible progress.
Jordan Wilson [00:15:59]:
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 gonna 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, bio ml. But then we have even, like, smart companies focusing so much time on just, like, using large language models to write, like, better LinkedIn post. Right? And and and things like that. Right? Like, are you ever baffled, or maybe it's just me, but just at the disparity between of of the capabilities and then what the average, human is using this technology for, the average even enterprise business sometimes, I'm shocked.
Ron Green [00:16:58]:
I it blows my mind all the time. You know, it's that old saying that the 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 kinda 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.
Ron Green [00:17:19]:
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 gonna beat your business. It's people, you know, in businesses leveraging AI that are gonna take your business.
Jordan Wilson [00:17:48]:
I'm I'm 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 comparison because I'm like, you know, oh, like, AI is not gonna 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 Yeah. Ten, fifty, a hundred people.
Jordan Wilson [00:18:18]:
Right? Can you talk even just about the capabilities and and what, you know, nontechnical people or, you 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 the Right. In the full Fortune 500. Right? What what does the everyday nontechnical person in business have at their fingertips?
Ron Green [00:18:48]:
If I would argue that, the, you know, the premier sort of bleeding edge reasoning language models like Claude three point seven or, CHI GPT, 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 it's it's very difficult for me to think of a business that couldn't benefit from some aspect of that.
Jordan Wilson [00:19:49]:
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, like, oh, you know you know, we're just now getting licenses for, you know, Copilot or we're looking at licenses for, you know, ChatGPT enterprise. And I'm like, you you you're a a a $20,000,000,000 company. Like like, why are you there? Is is that day zero? Is it is it moving? Well, you know,
Ron Green [00:20:20]:
so so that's a great question. I mean, honestly, I think and 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 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 I give presentations all the time, and I'll ask, you know, big developer organizations, you know, raise your hand if you're using Copilot, and I would say or something like Cursor a r Cursor AI, some some type of coding assistant. Invariably, I get a split audience.
Ron Green [00:21:00]:
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 are as powerful as the end user. Right? And so if 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 mind. And that's just coding assistance. This there's this is gonna be applied more and more everywhere. But 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 as they look at their their mobile phone devices and, you know, Sirius still is bad you know, is dumb as a bag of rocks.
Ron Green [00:21:53]:
Right? And, you're like, I'm not so sure I believe this AI stuff is real. The problem is it's gonna 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 technically, it could have, like, two years ago.
Jordan Wilson [00:22:15]:
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 wanna follow-up on this this concept of of coding and and AI coding, which, you know, I I 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 gonna be building their own applications by the end of the year. And then the anthropic CEO, just yesterday, Dario Matti, said that in three to six months, ninety percent of code is gonna be AI. And within twelve months, it's gonna be a %.
Jordan Wilson [00:22:56]:
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.
Ron Green [00:23:15]:
You know, I I probably think I mean, Jario's estimates may be a little optimistic because, any type of generative AI solution at this stage, even in even with reasoning models, does need some oversight because it can lose the thread or hallucinate or make mistakes. You know, these these systems are not a monition. Within, you know, three to five years, I think, you know, 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? You could just you know, let's just let's just set the table really quickly. General purpose computers like, you know, 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 they're kind of unbounded from a capability perspective.
Ron Green [00:24:22]:
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 gonna go away and certainly not in the short term. It means the amount that we can leverage and get ROI from new codevelopment is going to, skyrocket. I mean, it's it's almost hard to exaggerate how much that is gonna change 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.
Jordan Wilson [00:25:33]:
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
Ron Green [00:25:52]:
That's right.
Jordan Wilson [00:25:53]:
You're gonna miss the game. Right? The bus has 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 gonna see this this hockey stick, in the in the next couple of months. How do businesses keep up?
Ron Green [00:26:18]:
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 an 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 but I see executives frequently have the false belief that they can build some generative solution and just plug it in, and they don't they they forget about how they actually use it. The fact that if you're working with Claude or ChattGPT or something, you're massage you're you're giving it a prompt, you're getting an answer, you're correcting it, you're you're having a back and forth.
Ron Green [00:27:08]:
And 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 generative 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 or 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 a system 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 gonna get the most ROI for a couple reasons. One is it's massive defense.
Ron Green [00:28:18]:
You've got this data moat 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 all this huge investment when you if you do just waste wait a year, 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.
Jordan Wilson [00:28:59]:
And, you 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
Ron Green [00:29:13]:
Yeah.
Jordan Wilson [00:29:14]:
You know, I'll come back in six months and say, how dare you not be able to predict the future, Ron. Right? But if generative AI, is is just the start, what's next?
Ron Green [00:29:24]:
I think I I think the big next step and, you know, you can have me back on the show in a in a bit, and I will talk about day one. It's gonna be this. It's gonna 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 it's it's and and and we're pretty close to this. I I think this is really best case a year, probably worst case five, median case maybe two and a half. And what we're gonna have is, just as right now where there are reasoning models that can code at an elite level or, solve math problems at, you know, an Olympic level, these models, we're gonna 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 gonna discover novel insights.
Ron Green [00:30:33]:
They're gonna make recommendations. They're gonna be able to be introspective on their own output and reason at a level that is so sophisticated. It is literally gonna have to dumb down for us the explanation so that we can understand it. And that is what I'm so excited about. That's where we're going to be at day one in my opinion.
Jordan Wilson [00:30:56]:
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.
Ron Green [00:31:09]:
This is awesome. Thanks for having me.
Jordan Wilson [00:31:11]:
Alright, y'all. As a reminder, that was a lot. This is one of those ones. I'm already gonna say it. You might wanna listen to this twice. You also might wanna go to our website, youreverydayai.com. Sign up for the free daily newsletter. I'm gonna have fun relistening 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.
Jordan Wilson [00:31:32]:
Thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.
Midroll [00:31:38]:
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 your everyday AI 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.