Everyday AI Podcast – An AI and ChatGPT Podcast - EP 340: When Will We Achieve AGI? One secret aspect holding us back.

Episode Date: August 20, 2024

Win a free year of ChatGPT or other prizes! Find out how.It's the trillion dollar AI question. When will we achieve Artificial General Intelligence? (And what the heck is it, anyway?) We'll give you the 101 on what you need to know, and one secret that could be holding the official discovery back. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on AGIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Definition of AI, AGI and ASI2. Evolution of AGI3. Impacts of Advancements in AGI4. Future of AGITimestamps:02:00 Daily AI news06:15 When is AGI coming?09:54 Machines performing tasks requiring human intelligence.13:10 Generative AI brings impressive outputs through language.14:04 Generative AI democratizes US AI capabilities; narrow compared.19:00 Correct prompting with PPP course at podpp.com.20:52 Big tech companies openly working toward AGI now.25:37 AGI development inevitable, desirable, surpassing human capabilities.28:16 Pre-2020, experts said AGI was 80 years away.31:25 Criticism of experts in generative AI misunderstandings.33:49 Has AI's definition of AGI changed?37:21 Definition of AGI has evolved over time.40:45 Partnership between Microsoft and OpenAI pivotal.45:21 OpenAI benefits from important Microsoft partnership changes.47:08 Tech companies must focus on AGI development.52:37 Future work, business, career with AI impact.Keywords:AGI, Artificial General Intelligence, OpenAI, Microsoft, partnership, AI development, AI startup, Anthropic, copyright infringement, Google's Gemini team, Token offering, AI models, USAID, ChatGPT Enterprise, AI pace estimation, AI evolution, Artificial Superintelligence, AI prediction chart, ARK Invest, GPT 3 technology, Traditional AI, Generative AI, AI democratization, AGI benchmark, ChatGPT course, AI startups, Big tech companies, AGI cost reduction, Future of work, Everyday AI podcast.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. When will we achieve AGI, artificial general intelligence?

It's a question that I think about personally a lot, but I think it's something that we should be talking more about. Because I think the definition of both artificial intelligence and AGI is changing by the day. And I think that there's maybe one kind of secret thing holding us back from achieving AGI. All right. I'm going to be talking about that today and more on Everyday AI. What's going on, y'all? My name is Jordan Wilson, and I'm the host, and Everyday AI is for you. It's a daily live stream podcast and free daily newsletter, helping us all understand AI.

And who knows, maybe AGI. so we can grow our companies and grow our careers. So if that sounds like you, maybe you are brand new here. Thank you for joining us. Make sure to check out the podcast show notes for more related episodes and to get to our website. You need to get there. If you haven't already, go to your everyday AI.com. Sign up for the free daily newsletter.

So yeah, this is a podcast and live stream. But we have a newsletter every single day, recapping both the show and literally everything else you need to stay ahead of AI to grow your company and to grow your career. It is a free cheat sheet. So you should be going there. All right. So I'm excited today to talk about when we will achieve AGI, or maybe if we will. All right, because I think some of what we're going to be talking about today is going to be

surprising you. So before we get into it, though, let's start as we do every day by going over the AI news. All right. So authors are now suing Anthropic over alleged copyright infringement in AI training. So a group of authors has filed a lawsuit against the AI startup Anthropic, claiming that it has engaged in quote, unquote, large scale fact by training its chatbot clawed on pirated copies of copyrighted books. This marks a significant moment in the ongoing legal battle surrounding artificial intelligence and copyright issues. So the lawsuit was filed in federal court in San Francisco by authors Andrea Bartz, Charles Graber, and Kirk Wallace Johnson, who seek to represent a class of authors in similar situations.

This is the first lawsuit from writers specifically targeting Anthropic, despite many other lawsuits in similar cases, against its competitor, OpenAI, the creator of chat GPT. The authors accuse Anthropic of violating copyright laws by using a data set known as the pile, which allegedly contains a significant. a number of copy of pirated works. The lawsuit argues that anthropics practices contradicted its claim of being a responsible developer of AI technology, stating that the company profits from strip mining human creativity. All right. Our next piece of AI news, Google and its Gemini team announced access to 1.5 billion free tokens a day. Yes. So the Google Gemini team announced just kind of on the download on Twitter.

And it's making headlines by offering an unprecedented amount of free tokens, 1.5 billion tokens for free each day for developers. The move that stands to transform the landscape of AI development for both aspiring and established developers. So this follows closely after essentially Open AI said a similar thing. Hey, it's free until the middle of September. So the Gemini 1.5 Flash free tier grants developers 15 requests per minute, 1 million tokens per minute, and 1,500 requests per day.

That means that the developers can experiment and build applications without immediate financial pressure fostering innovation in the AI space. In addition, this new free tier includes context caching for up to 1 million tokens of storage per hour and free fine-tuning capabilities, which are crucial for developers looking to optimize their mobile, their models for specific tests. Y'all, I feel bad for companies that were super early to this generative AI boom, like two years ago. A company doing this would have spent probably millions of dollars and not done a very good

job. And now it's too, I mean, companies are making intelligence and compute too cheap to meter. All right. Last but not least, Open AI has announced, or it's been reported, that OpenAI has its first federal partnership with USAID to implement ChatGPT Enterprise for federal use. So OpenAI has made headlines by partnering with the U.S. Agency for International Development marking U.S. aid as its first federal agency customer for ChatGBTGPT Enterprise. So OpenAI's ChatGPT Enterprise is designed for larger organizations providing advanced

analytics and customization features that can enhance efficiency in government operations. So the company is pursuing FedRamp moderate accreditation, which would enable ChatGBT GBT Enterprise to handle moderately sensitive federal data expanding its potential use within the government agencies. The U.S. White House Biden administration's recent executive order encourages federal agencies to adopt generative AI technologies while addressing concerns about data security and bias. So yeah, pretty big news there.

federal government kind of striking an official partnership with open AI in a federal agency will be using chat GBT, the Enterprise edition. And hey, by the way, if you have Chad GBT Enterprise, if your company does, do you know we train companies on that? It's like one of the big things we do, FYI. So yeah, reach out. Reach out if you or your team needs help with chat. All right.

So let's get into it, y'all. Let's talk about the big thing here. When will we achieve AGI? And I think one kind of secret relationship or one kind of fine print that might be keeping development back. All right. So I'm super, super excited for today's conversation. So I'd love to hear from our live stream audience. Yeah.

Hey, podcast audience, you know, you might get tired of, I don't know, hearing questions from the live stream audience. You might be like, oh, I wish I could get my questions answered. we'll join us. We do this live every single day at 7.30 a.m. Central Standard Time. So, yeah, let me know, everyone. What are your questions on AGI? Do you think we're going to get there? What do you think is holding us back right now?

All right. So let's just go ahead and start at the end. I'm not going to make, I'm not going to make you wait. All right. But I think right now we are much, much, much closer to artificial general intelligence, than most people think. All right, don't worry. I'm going to get to what it means,

the definitions and the differences between. Don't worry. But one of the reasons why I think we haven't, quote, unquote, officially achieved AGI or artificial general intelligence is because the goalposts are constantly moving. All right? I'm going to have a little bit, you know, we always bring receipts, y'all.

It's hot take Tuesday as well. I should have called that out, y'all. So let me know. Should I be middle of the road or should I really bring the heat? But here's what I think is holding us back. It's actually some of the fine print and some of the details between open AIs partnership with Microsoft. All right.

So that's high level, y'all. And we're going to dive into it now. But I think if we were using old standards, if I'm being honest, I think we would already technically have achieved AGI. But the goalposts are always moving. I think as large language models are getting more advanced, I think the goalposts are moving on what AGI even means, artificial general intelligence.

All right. It looks like Michael said three flame emojis. So we'll see. Maybe we'll keep it tame. It looks like everyone wants a tame show today. That's fine. We can do that. All right.

So let's go ahead and put some definitions out there. All right? Because, yeah, maybe if you are new to this whole AI scene, generative AI, maybe you're not sure. Maybe you don't know what AGI even is. So let's go ahead and define it. Okay? Yes, because the definitions are constantly changing. So let's take a look at artificial intelligence, artificial general intelligence, and then artificial superintelligence.

So let's start with AI. And let me just start by saying this. AI is not new, right? Artificial intelligence has been used in many different industries for decades. It actually goes back to the 40s and 50s. And it's been widely used by many industries since the 70s and 80s. Artificial intelligence is not new. We've had machine learning.

We've had kind of this deep learning phase as, as well. So artificial intelligence by itself is not new, but let's go ahead and define it. So these are my definitions, too. All right. So artificial intelligence is when machines or software are designed to perform tasks that typically require human intelligence, such as recognizing images, translating languages, or making decisions. And I will say traditional, quote unquote, traditional artificial intelligence is really based on a set of rules, a set of hologmages. A set of algorithms, decision trees, right? It's programmed. There's bits and bites almost, right? Traditional artificial intelligence. So like I said, an easy example that's been around for decades,

right, is when banks are giving out loans. And they have essentially algorithms, right? You go into an office, you probably give someone information, you fill out a form, they enter that form, and then there's an artificial intelligence algorithm that says, okay, is this person really qualified for this loan or not, right? Maybe if it's a big loan, you might have filled out a ton of paperwork. So there's a ton of different pieces of data that you're essentially giving the bank. And then the bank uses artificial intelligence to essentially assign different values and to see if you are risky, too risky for the loan.

Right. So AI has been around for a very long time. It's not new, right? And you've probably been exposed to artificial intelligence, even in your daily lives, well before chat GPT. All right. But obviously, I will say this. And I was at the Nvidia GTC conference a couple months ago.

And its CEO, Infidia CEO, Jensen Wong, kind of said that, you know, generative AI in large language models marks this new era of artificial. intelligence. And I absolutely agree, kind of this generative AI, right? So generative AI is a little different than artificial intelligence, kind of, you know, quote unquote, old school. But, you know, generative AI, not to be confused with artificial general intelligence is different. Generative AI, it brings, it democratizes, right, AI for everyone. Because before, let's say, 2020, right? Yes, Chad GPT was you know, released to the masses in November 2020. And I do think that's the turning point. But, you know, this generative AI technology was available through other providers. You know, OpenAI made their technology available to third party developers back in 2020. So pre-2020, you know,

you could, I say that's traditional AI, right? Yeah, I'm slapping my own labels on this, right? Now we have generative AI. It is kind of this next advancement of artificial intelligence that lowers the learning curve to like zero, y'all. I mean, to take advantage of artificial intelligence pre-2020, I mean, if I'm being honest, you either had to be a specialist working at one of these companies that had niche use cases for artificial intelligence, or you had to be a deep learning, machine learning expert, right? You had to have a degree in artificial intelligence to essentially take advantage. So generative AI, so let's say, you know, the 2020, to, you know, now range.

That brings AI to all of us, right, through large language models. And what generative AI is, simply put, is when anyone can simply speak or type to an AI system and get a pretty impressive output, right? These large language models, these generative AI systems, you know, like chat, GPT, Google Gemini, Anthropic Claude, you know, and then you look at more image or creative. based tools like runway, like Mid Journey, like Dolly, Adobe Firefly, et cetera, right, where you can put in a simple text prompt or speak in many cases and get something visual, right?

You can get a photo. You can get a video. You can get an audio track, right? You can get a voiceover. So this new generative AI phase, not to be confused with AGI, has really democratized how the U.S. works and what we can all accomplish. with artificial intelligence. But it is but a small footnote in the larger umbrella of artificial intelligence.

All right. So now let's look at what artificial general intelligence is. All right. So this is a type of AI that can understand, learn, and apply knowledge across a wide range of tasks, similar to how a human would effectively performing any intellectual task. Okay. That's the difference. For the most part, AI.

or generative AI, if you will, performs a more narrow, right? Not even going to get in a narrow intelligence, but AI, generative AI, performs a narrow base of tasks or a narrow set of task, right? Hey, recap this PDF. Hey, you know, write this blog post. Hey, create this image, right? You're kind of working on one task at a time, and it's a task that, you know, that kind of the AI system has clearly been trained on.

So AGI is slightly different. That is when a machine or a system or, you know, who knows, a software, right? What shape AGI will eventually take. But that's when it can perform a broad range of tasks at the same or higher level than a human. Right. Now let's talk about artificial superintelligence, right? We got to get the whole acronym soup here, y'all. So artificial super intelligence or ASI is more of a hypothetical AI that surpasses human

intelligence in all aspects, including creativity, problem solving, and emotional intelligence. ASI would be capable of self-improvement. That's a big thing and could potentially outperform humans in any cognitive task leading to profound implications and potential risks. All right. I simplified it here. Think of AI like this. AI is like, oh, look at what the machines can do.

AGI is, oh, the machines are way better than my job than me. And ASI is, oh, be fearful of the machines. All right. That's a very oversimplified way. But that's a way that I think about it in my mind, right? And yeah, we've been at this, I think we've been teetering, right? If I'm being honest, we've been teetering on this AI, AGI line, I'd say for the past six months, right, between like, oh, look what the machines can do and,

oh, is this AI actually better than my job than me, right? And I think that, again, if you are looking at narrow applications, I don't think there's any denying that with certain skill sets, AI is way better, way, way better than a single human, right. So let's just say if you are a single human and all you do is data analysis, right, all day. A.I. is way better than the best human out there, right? But that's a single task. That is a narrow focus. That doesn't mean that you can talk with an AI system and it can literally do anything and everything that a human can do. That's kind of when we talk about AGI. That's kind of the benchmark, right? It can perform.

any task without the need to train it, without the need to necessarily show it examples, right? We're essentially with a zero shot prompt or with little training, with little upfront information, you could type or chat or interact with a system that automatically is going to outperform almost the best human on any task. I think we're getting close. I think we're getting a lot closer. All right. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience.

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Every step the assistant takes is visible, so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. So now, now let's talk about some of the reasons why I think development and the distance between where we are now in whatever that AGI finish line is diminishing quickly, if I'm being honest. And I think one of the biggest, biggest reasons, actually, is now you have the tech titans of the world, just racing toward AGI. Literally.

And let me just call this out. The concept of a company pursuing or contributing to artificial general intelligence, AGI, was straight up taboo, right, 10 years ago. You know, Open AI was actually, you know, in 20. 2015, it was one of the first companies that was saying like, hey, we're working toward AGI. I mean, at the time, they were a little known startup. Open AI was not what it is now back in 2015 to 2019. Very few people, even very few people who worked in the tech space knew what Open AI was,

unless you had a niche in or around AI or were particularly interested. you didn't know Open AI or that this small startup, you know, was working toward artificial general intelligence. But now it's different. It's different, y'all. And let me just say this. Up until two-ish years ago, you did not have the largest companies in the world openly working toward AGI.

Like I said, 10 years ago, it would have been taboo. for a big tech company to say this. It would have been controversial. But I think through this, we'll just call it this experiment over the last 18 months, maybe since ChatGBT was released. Now companies and the U.S. economy sees and understands the value of large language models and the value of businesses using AI top to bottom. So not that it's become trendy to work toward or talk about AGI,

but I think that the business world has opened its eyes, I think because they see the dollars, right? The more you talk about AI and your earnings calls, the higher your stock price goes, right? But now you have all, not all, but almost all of the four or five of the top seven largest companies in the U.S. openly either chasing AGI or openly collaborating toward achieving AGI. Like I said, this is straight up taboo 10 years ago. Right. If a C-suite executive said, yeah, we're working toward AGI, it would have been a red flag, right? They would have, the board would have put out a memo.

It would have been bad. Now it's almost the opposite. Now if you're a, you know, a tech conglomerate and you're not openly working toward AGI, the board might just say, why? Why not? This is something you should be doing. Whereas before it was, oh, no, we shouldn't be doing that. When we achieve AGI, what happens to our jobs? Right.

Let's look at the proof. Invidia, they've been the largest company in the world. Now they're top three. They provide the hardware and software and tools for AI development. they support AGI research through partnerships and infrastructure and their CEO. I was literally in the room a couple of feet from Jensen Wong when he said this. He did say that we would achieve AGI within five years. Okay.

Let's keep going. Microsoft is collaborating with Open A.I. On AGI development. And Open AI has obviously been the leader in the pursuit of AGI. Or one of the leaders. All right. I'll say they're probably some of the first. Okay.

And Microsoft sees AGI as the next step in AI evolution, and they're actively researching AGI technology. Y'all, it's two of the three largest companies in the world. Although they're not, you know, that's not their mission. Achieving AGI is not their mission. They are supporting the very companies that do, and they are collaborating and they are researching it. Meta.

All right, here we go. Top six company in the, in the U.S. Meta, formerly known as Facebook, right? They are actively working on AGI. They have a new project just announced to build an open source AGI, and their CEO, Mark Zuckerberg, views AGII as a key company goal, right? He's really shifted his focus from, you know, it was social media a while ago, you know, during Facebook's early days.

Then it was the Metaverse. And now there's been a very hard pivot over the last year and a half toward not just artificial intelligence, but artificial general intelligence. Okay? Yes. The CEO of one of the largest and most powerful companies in the world said achieving AGI is a key for his company. Would have been blasphemous 10 years ago, y'all. Anthropic.

All right. So Anthropic believes that AGI is imminent within a couple of years. They estimated human level AGI by 2030. And even the chief of staff that's made big headlines a while back. Even the chief of staff believes that AI might take her job within three years. Yeah, the chief of staff, the person in charge of staffing, one of the most influential and powerful companies in the AI space said that. Then we obviously have open AI. They've been actively pursuing AGI since their inception.

That is part of their mission, is achieving a safe AGI. So they believe AGI is inevitable and desirable, and they aim to develop AGI that outperforms humans on most tasks. And kind of their definition of it is a highly autonomous system that outperforms humans at most economically valuable work. All right. More receipts, y'all. Let's talk about this. We're going to take a little bit of a historical view here and talk about why these AGI produce. predictions keep changing, right?

So I just mentioned there, Anthropic, OpenAI, Meta, NVIDIA, some of the either CEOs or leaders of these companies are saying, we're going to see AGI in a couple of years, maybe five years, but definitely by the end of the decade, which, if I'm being honest, can be a scary concept, right? because it very quickly reshapes, AGI does, reshapes what humans are capable of and what machines are capable of. And that obviously has both swift and longstanding impacts to society, to business, to our worlds, right? I'm not out here, you know, speaking in hyperbole. that's the truth.

So let's talk about why these AGI predictions keep moving. All right. So a very kind of famous graph here, y'all. And hey, for our podcast audience, this is one of the ones where you're going to want to check the show notes. And come back and maybe watch this on YouTube or LinkedIn. And you can leave a question too. And I'll do my best to answer it or tag someone that can. All right.

But this is one of those charts I think you have to see. But I'm going to do my best to describe it. So this is a chart from ARC invest. Okay. And the title of this chart is expected years until a general artificial intelligence system becomes available. All right. So essentially, these are predictions charted over time on when leading experts,

so these are averages, essentially how long until leading experts say that we will achieve AGI. Okay. So if you look at before GPT3, so like I said, the GPT3 technology was actually introduced in 2020. It made available, right? Not, I'd say most of the world didn't know about this technology until chat GPT in 2022. So if we look at pre-2020, the average expert said it would be at least 80 years, okay?

Let me repeat this. This is five years ago. The average expert said it would be 80 years until we achieved AGI. Okay. And then this graph here from ARC Invest kind of plots different, different key milestones, and then how those milestones seemingly impact these experts, right? because this is, ARC Invest, I believe,

was doing these studies on an ongoing basis and plotting them on an ongoing basis year by year. Or it looks like actually multiple times a year. All right. So then we look, when GPT3 was announced, that 80 years went to 50 years. Okay. And then Google kind of got on board, right,

with its Lambda models. All right. And then it went from 34 years, the average, oh, how long is it going to take? One from 34 years to 18 years. All right. Then chat GPT launched shortly there, followed by GPT4, launched the premium and more capable version.

Now, and, you know, I'm going to be interested to see the next time this chart is updated. So essentially in late 2023, now we're at eight years. So again, post-chat GPT, post-GPT4, now the average expert kind of forecast for when we will officially achieve AGI is now a little under eight years. Okay. So again, let's talk about this. In five years, y'all, in five years, the smartest people in the world, the biggest, brightest experts.

In five years, they went from saying we are 80 years from AGI to now we are eight years. All right. And if I were to guess, I would say within a year, this chart will, the average will be three years or less. All right. And then you kind of see, you know, if this forecast error continues. that would plot this at about the end of 2026 or 2027, early 2027, so in less than three years. Or if the forecast continue as they are now, essentially it's the end of the decade, right?

We bring receipts here, y'all. We bring receipts. But I will also say so many experts don't understand AI. They don't understand generative AI. They don't understand large language models. They are, you know, future casting and futurists. And, you know, they're talking about in theory, I wouldn't, well, maybe at this point I'm an expert, right?

I put out hundreds of podcasts, live streams, thousands of hours of contents, talking to some of the smartest people in AI, in the world, and bringing them to you all, too, as well, where you can ask them questions and learn from them. I'm surprised sometime, right? When I read reports or I read people who write these papers on AGI and AI, and I'm like, these people have no clue what they're talking about. I think even the average, quote unquote, expert is disastrously misinformed or ill-informed.

And I think about AGI and AI development. And I think this chart proves that. This chart proves that that the quote-unquote smartest experts in the world, we're laughably underestimating the power of artificial intelligence in the pace of its development. I think it is going faster than we understand. All right, y'all. And hey, if you do have questions, let me know. John's asking if you think we can get Sam or Jensen on the show.

Well, we'll see. Maybe in the future here. We'll see. That would be great. All right. Yeah, Brian. same. Yeah, if I see one more AI bubble article, I did a full rant on AI's not in a bubble, all right? So let's keep going here. We're not going to keep this one going forever. This isn't going to be an accidental two-hour episode.

But I want to talk again a little bit more about this concept of the goalposts are constantly moving. And you know we bring receipts, y'all. So I went back in the archives, right? So I was reading articles from as far back as Google caches could find. Right? So I was reading articles from 30 years ago. Yeah, some of the earlier ones. The website design was good. You know, so reading articles from 30 years ago on what is AGI. There weren't a lot of them. But I read many of them from 30 years ago, 20 years ago, 10 years ago, because I wanted to see something. I wanted to see something. Has the very definition of what artificial general intelligence is, has it changed? Right.

Has it changed as AI becomes more powerful? Are we intentionally moving the goalpost back? Maybe because, I don't know, maybe because once you achieve AGI, it just makes things weird. So instead of saying, okay, hey, it looks by, like by most definitions, maybe we're there. Instead of saying that, we just keep moving the goalposts and saying, oh, okay, well, maybe it means something else.

All right. So this was from. this was from the machine intelligence research institute. And this article was from about 10 years ago. All right. And here's essentially a shortened version of what their definition was 10 years ago. So I'm summarizing here. But it said artificial general intelligence is the ability of a system to learn and solve problems

across different areas, much like a human. AGI can achieve complex goals in various situations while using limited resources. AGI can also apply knowledge from one domain to another rather than just being good at specific tasks. Huh, weird. I don't know, at least according to this article, which it seems like one is one of the most prominent, you know, articles or pieces of research around AGI 10 years ago. I don't know. If I'm looking at these bullet points, I'm like, yeah, we're there.

Right? Yeah, we are. All right. Here's another one. This one was from about 11 years ago. Sam Altman, maybe you've heard of him. He used to blog a lot. All right.

So this article, Sam Altman essentially said, all right, I'm paraphrasing here. But we'll put it in the newsletter. You can go read it for yourself. But he essentially kind of defined artificial intelligence as something that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks similar to human cognitive abilities. Uh-huh.

Aren't we kind of there? Right? So the goalposts keep moving. So it's like, have we achieved AGI? I don't know. Seems like we keep changing what AGI is because at least according to some of these things from 10 years ago, right? You can look at these and be like, yeah, we're, yeah, we're here.

Obviously, as researchers learn more, they start to set maybe more detailed and specific, I guess, boundaries or milestones on what achieving AGI even is and what it means. Sure, I get that. You know, 10, 20 years ago, I think AGI was actually, for the most part, a little more theoretical, right? and then as we actually get closer and closer to potentially achieving artificial general intelligence, then we start to set stricter guidelines on what are the hurdles to clear in order to officially say that we've been there. All right. We got two more points here, y'all, and these are big ones.

So here is, I think, one of the kind of secret reasons that may be why we haven't technically achieved AGI. And I think it's actually this Open AI and Microsoft partnership. Okay. So I'll give you this super high level overview here. So this partnership between Microsoft and OpenAI, reportedly Microsoft invested about $13 billion and has a 49% stake and 49% ownership stake in Open AI. However, once the Open AI board says, that it has achieved AGI, that agreement changes.

All right. And keep in mind that Microsoft did previously have a seat on the board at OpenAI, right? Oh, interesting there. It no longer does. All right. So interesting, right? Let's keep going. So essentially, Microsoft doesn't have any stake in OpenAI.

future AGI tech, right? So again, I'm sure they have legal documents, right, that are, I'm sure hundreds of pages long that aren't publicly available. But what is publicly available is essentially future AGI technology from OpenAI. Microsoft does not have a stake in that, right? So that is when their current partnership could start to change. And I guess it obviously depends on what's in those documents that the rest of the public does not really have access to. But my question is, how do they separate it?

Because my outsider's viewpoint, right, I would consider myself a very informed outsider, but I'm an outsider nonetheless. So take my hot take here on hot take Tuesday with a grain of salt, right? But I feel, you know, and I believe. this investment was made about five years ago. I think at the time neither company could foresee what this partnership would mean and how big artificial intelligence and large language models, how pivotal they would become in our day-to-day operations, right? I guess maybe best case scenario when Microsoft made this large investment. Maybe they said this is going to fire power the future of our operating system, right?

Maybe they said best case scenario. Maybe they were just taking out an expensive flyer on very promising technology. But think now, if you're a power Windows user, you're probably using Microsoft 365 co-pilot, right? Maybe your organization is dependent on this co-pilot technology that is baked into the operating system of Windows. Windows, which is, guess what? Powered by OpenAI's GPT40 technology. So I don't know if when this agreement was originally staked, if either company could have seen into the future and could fully understand how important this partnership would be to not just both of their respective companies, not just to the business world, but to the U.S. I don't think people understand. And I've gone over this in death. I'm not going to go over it again, right? Essentially, I said, hey, if you're a knowledge worker out there in the U.S., you have no clue, but you are constantly using Open AIs technology, you just don't know it. And your daily lives and your personal lives and your business lives, your company, the software you use, everything, whether you know it or not is somehow powered probably by Open AI. But also, if Open A.I.

If their board says, yes, we have achieved a GI, we have the technology, how do they separate it? Right? Open AI made a pretty big and I think a smart move when they came out with GPT40. And O means Omni, where essentially everything is being done under the hood by a single model. And presumably, Open AI is working very hard to make that singular model more and more powerful. and to give it, in theory, capabilities that would reflect artificial general intelligence. So how do they separate it? Right.

We know that the partnership is both as important, I would guess. It is equally as important to Microsoft and to Open AI. So what would be Open AI's reason to come out and say, yes, we have achieved. AGI, therefore, we must somehow restructure our technology. We must separate our technology. We must dilute somehow or start to dissolve or rework our partnership with Microsoft. What incentive do they have to say that, to do that, right? And also, Open AI, achieving AGI has been mission critical to them.

And they've raised billions of dollars. I don't know. Maybe I'm speculating. here because it's hot take Tuesday. But if I'm an investor looking to potentially invest hundreds of millions or billions of dollars into Open AI, right? There's been reports that Sam Allman is looking to raise $7 trillion for some future projects centered around intelligence centered around, compute power, et cetera.

I don't know if said company OpenAI has quote unquote achieved its mission. If one of its founding missions is to achieve AGI and the board comes out and says, we achieved AGI, doesn't that make fundraising exponentially harder for Sam Altman and Open AI? I'd say yes. So what incentive them, right? Because if Open AI comes out and says, yes, we've achieved AGI, it's great, right? Big box checked off.

Huge step for humanity, huge step for technology, huge step for the future of business. What does Open AI actually have to gain? I don't know. I'd say they might have more to lose to say that they've officially achieved. AGI, right? So I think, and I wouldn't blame them, I would say Open AI continues to move the goalposts because Open AI, I think, also loses in some way, shape, or form again. I could be ill-informed because I don't have access to the documents and, you know, the public doesn't as either. But it is clear that the partnership changes, the Open AI Microsoft partnership

changes. So yes, Open AI has greatly benefited from the funding and the support in the architecture that Microsoft has provided. But at the same time, I think Open AI is benefiting in a huge way as well. If I'm an Open AI executive, I don't want to lose that partnership. Think of how much insights they have to gain, right, when they are presumably getting reports back from co-pilot users to improve the GPT4 technology. That's some of the most important information from a startup is essentially when us humans are telling them what's a good output and what's not.

That saves them years of development time and probably billions of dollars of development time in the end. Okay? So we have to think about that. All right. Let's wrap this thing up, shall we? So why are we closer than ever? Why are we closer than ever to AGI?

Well, like I talked about. It has been unprecedented up until this point. that some of the largest companies in the United States are openly working toward achieving AGI or actively supporting and openly supporting those companies that are doing that. Like I said, a decade ago, more, you know, 15, 20 years ago, it would have been straight up taboo. As a big company, as a public company to say, yeah, we're working toward AGI. people would have called you an apocalyptic crazy in your stock if you were a public company would have went in the toilet.

Now, now if you're a big tech company like Amazon, like meta, like Microsoft, you almost have to either be explicitly and outwardly working toward AGI or indirectly, you know, working there. Because I think the dollars have started to make sense. of what AGI means to business and to the U.S. economy. Someone write that down. That was good, right? Yeah, this is unscripted and unedited.

Aside from some bullet points I put up on the screen. All right. So that's number one. Also, two of the most powerful AI startups in the world are either directly working toward it or indirectly supporting it in OpenAI and anthropic. And then last but not least, y'all, and this was kind of, kind of reference at the beginning of the show when we talked about Google Gemini giving away 1.5 billion tokens per day for

developers. That's wild. That's wild, right? And then also, Open AI similarly said, hey, GPT40 Mini, it is free. They announced this more than a month ago. They said it is free to fine tune through essentially the end of September. So these companies have essentially been giving compute. They've been giving tech like this technology, this, this inference power.

They've been giving away intelligence for free, right? It is now to the point, right? There's this saying in the AI community, intelligence too cheap to meter. one of the biggest obstacles to achieving AGI 5, 10, 10, 15, 20 years ago was the cost. It was the cost in the availability. If you were a tech company 10 years ago or if you were a scrappy startup 10 years ago and if you wanted to work toward AGI, it would be nearly impossible. It would be nearly impossible, right?

Now, not so much. Right. now, if you want to give AGI-esque capabilities to your business, right, it is essentially free to do right now. You, y'all, like 10, 15, 20 years ago, you had to have millions, millions or billions of dollars. It's free now, right? For businesses at least, right?

Obviously, these companies, this is a cost for them to bring people into their platforms and to keep them there. But there's a race for this. But also even for these companies, the cost of compute has gone down exponentially, right? Essentially GPU chips. So these chips that these big companies need to create next generation AI, 10 years ago, the chips used to be much more expensive and much less powerful. The cost of compute is going down exponentially.

most estimates say, even in the last couple of years, it's gone down tenfold, tenfold. The same thing with cloud computing, tenfold in years. So the rate at which intelligence and GPUs and the cost to get to AGI has gone down exponentially. In the last, geez, just the last couple of months, it has turned. turned into a sprint, into giving companies essentially intelligence, giving them compute for free. All right. We are in unprecedented times, at least when it comes to AI development, and if and when AGI is possible.

All right. I'll wrap it up by saying this, y'all. I'll wrap it up by saying this. I think we're going to be there very soon. I think we are going to achieve AGI very soon. I just gave you all the receipts. If we're looking at how AGI was commonly defined 10, 15, 20 years ago, we've already achieved it. The definition of today, I think will be there in less than five years.

It's a given. But will that definition continue? you to move. I would say so. But y'all, whether you are just an individual who's interested in AI, a business leader, or maybe you are someone who's working in the AI space, H.EI is inevitable, right? You saw on that chart five years ago, they said we were 50 years out. Or they said, no, sorry, they said we were 80 years out five years ago. Now they're saying eight. We've gone from an 80-year projection to an eight-year projection. And like I said, I think when the next time these charts are updated, it's going to be like

three years. We are going to get there very soon. All right. So what does that mean for the future of work? What does that mean for the future of business? What does that mean for your future career? I don't have those answers yet. But that's why we're going to be here every day helping you figure that out.

Bringing on experts from a question. the world from all these big tech companies. We're giving you examples of how companies large and small are growing with generative AI, how people are changing the trajectory of their careers. Real use cases, that is what everyday AI is all about. All right. So when will we achieve AI? I'd say sooner than we might think.

Like I said, by old definitions, I think we're already there. But I think it's going to be a matter. of years. All right, I hope this was helpful, y'all. If so, please repost this, share this with someone in your network. I don't know. Maybe if you do, I'll send you even more spicy takes that were too spicy for this show. All right. So if this was helpful, please let us know. If you're listening on the podcast, please click that follow button, leave us a rating if this is helpful. We put so much time, energy and effort into cutting through all the nonsense and giving you what actually matters. So also make sure if you haven't already, go to your everyday AI.com, sign up for our free daily newsletter and check out the thanks a million giveaway to celebrate the Everyday AI podcast hitting a million downloads.

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