A few logistical things. One, I'm carrying a magic trackpad because everyone has clickers. What if we had multiple dimensions? We're going to experiment with this today. And two, I'm using AI, like fancy new everything, right? So this is Tome, and we're going to go two-dimensional with our slides as well.

So I'm here to talk about the AI engineer. You're all here because you believe that there's some value to this idea. And then I just put like a ridiculous 1000x on this. But I do think there is some meaning towards thinking about higher orders of magnitude towards raising your ambitions.

And that's what I would like all of you to do today and to do with your friends back home. So and obviously a lot of AI-generated art because, I mean, it's an AI conference. We've got to do it. We've got to do that. First of all, I want to congratulate you on being here.

I'm not talking about here location-wise, physically. I'm talking about here in terms of the point in time. Imagine if you were a mathematician. When was the best time to be born? I would propose around about 600 AD, this dude, Brahma Gupta, he invented zero. Pretty novel invention that took us only 4000 years to do that.

But there's certain times where if you're in that field, you have to be there. That's the thing. When was the best time to be born? There's a right answer. 1905, 1927. And this conference kind of is inspired by the Solvay Conference. That's Albert Einstein, Marie Curie, and a lot of people that you just saw in the Oppenheimer movie.

Same thing. If you made cars, there was a right time, 1900 to 1930. If you made personal computing products, 1980 to 2010. If you're a millennial, if you're very online, you ever get these memes like you're born too late to explore the earth, born too early to explore the stars, you're not too late.

We are here. This is based on demographics and history, the approximate timeline of all of humanity. We know that we're roughly about 73% of all concurrent intelligences if we don't expand our own intelligences or go to other planets. So my argument and my message to you today is that you are just in time, and the timing is right to 1000x.

I think a lot of my technology and industrial organization thinking is informed by Carlotta Perez, one of the most influential thinkers on tech revolutions. She wrote this book about the installation and deployment periods of tech cycles. And we're definitely going through one today. A lot of you on your mind here, I know you're here, but also mentally you're back home thinking, how much of this is a fad?

How much of this is Web 3 again? And we've seen this over and over. the historians greater than us have explored this over the industrial revolution, the age of railways, age of heavy engineering and steel, oil, and most recently the tech revolution. Funny enough, they all roughly span between 50 and 70 years, and if you're around in that time, that's the field to be pursuing.

So when did the AI revolution start? We're very lucky. It's very hard historically to place a start point on something that changes human civilization. We have a moment, 2012, AlexNet. We're roughly 10 years on. And we can put numbers to it, right? So most of the time these curves are sort of theoretical.

They're just kind of like, bleh, like y-axis is just bleh. Here we can actually just put the amount of compute we're using towards training in models. There's a huge inflection, that's AlexNet, right on the blue dot over there. That's a huge inflection where we realized, gradually realized, it took too long to realize, but scale is starting to work.

And if you actually take this out, a lot of people have been taking this out, and I want you to take scaling seriously. There's three reasons why six is a magic number. There's a very famous investor who I shall not name, and says, "Imagine roughly 690 in moral compute by the end of the decade and plan for that." So there is more of this coming, linear projection-wise, and you can plan on a lot more investment in language models.

John Carmack says there's six key insights towards AGI. And lastly, George Hotz has these really nice analogies. GPT-3 took about one-person year of compute. GPT-4 took about 100-person years of compute. You stretched it out to GPT-10, the difference between GPT-4 and GPT-10, again under the six-fold increments in GPT advancements.

And that would be more compute than the equivalent compute of every human ever who ever lived. So just being in the right moment, you will get to live on top of these mega, mega trends that is greater than any single one of us. And I think you're all here thinking about the AI engineer.

And I put it in a very, very small sort of local context of, hey, what's the org chart? Where do the ML engineers sit, where the ML researchers sit, where the software engineers sit? And what's the gap that's opening? It's the AI engineer. It's very much of a demand and supply argument.

There's something like 100,000 card-carrying data science machine learning engineers. And GitHub claims to have 100 million registered developers. I don't know what the real number is. You can debate 40 to 50 million to 100 million. It's orders of magnitude more. So we think there's going to be much more AI engineers than ML engineers.

There's all these reasons why. Same reasons that I mentioned in the blog post that you've all read. And also, why engineering and not just prompting is because LLMs themselves are not AGIs yet. We actually have to coordinate them in systems of software. We have to write code around them and orchestrate them with code in order to do something useful.

And we already know how to do code. So I want to spread it out a little bit more. I think that the conversation on AI engineer has a vague discrepancy. And I want to basically split it out into three areas of AI engineer. Software engineer enhanced by AI tooling, like Co-Pilot.

Software engineer building AI products, like Mid-Journey. AI product that replaces human engineer, potentially like Auto GBT and maybe Replica Ghost Rider. So let's give these guys a name. And in case you're wondering, enhanced by versus replaces, I think about it very much like the self-driving car terms. Like level two, level three.

There's a difference between whether humans in the loop were there or humans as to fall back. So let's name it, three major types of AI engineer. The AI enhanced engineer for people who are enhanced by AI. People who build AI products, AI products engineer. And then the AI engineer agents who is not human.

And naturally, of course, if you're interested in sort of progressing up the career ladder, AI enhanced engineer, then product engineer and engineering agent. So this talk was really inspired by actually Amjad, who is speaking next, where he did a recent talk with the XTNCD podcast, and Sam Altman, who actually sees 1,000 X engineers in OpenAI every day.

And it's really a set of stackable 10 by 10 by 10 improvements. Over the course of the next two days, I think you'll be seeing a lot of the speakers will be working on different parts of this stack. So I really encourage you to think about where in your life this AI movement can improve and increase your productivity.

I'm very, very honored to have drawn from all over the world the leading eight lights of the AI engineering movement. We are a very small room today. I do think we can 100 and 1,000 X from here. And it's not just about tools and speakers, it's also about you.

So I highly encourage you to take part in all the opportunities that we have for you to mix and mingle with each other, with the speakers and with the sponsors as well. So there's that. The final word I do want to offer you is effectively what I think, in terms of non-technical terms, the 1,000 X engineer could offer.

My favorite advice for what a 10X engineer could look like is an engineer that teaches 10 other people what they know. networks, that's not a technical term, but it is very useful. And there's all these scaling laws for networks, which I really keep in mind. So you can go from O of N to O of N squared to O to the power of N.

But really what O of N is, is you attending all the talks and consume all the content and letting people in with your Pac-Man rule. O and Squared is helping others learn. My very first blog post was at exactly a conference like this where I was encouraged to write something.

And of course it was on machine learning. And finally going home and then building your own networks of AI engineers and helping to grow networks of learning as well. So I hope you take that with you in your AI engineer journey. I hope that over the next few days you get a sense of what it's like to be at the start of an industry.

And I'm just glad to be here with you. Thanks so much. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. I'll see you next time.