You're either you're right or you're wrong.

And I actually think that teaches you some of the wrong lessons.

I remember really vividly from my early violin lessons where you can get all the notes right, but that actually isn't what matters.

What matters is that you could weave through the notes, the emotion and the story that the original composer is trying to convey.

And I think that was a really powerful lesson 'cause I think one thing that many of us learned over time is that a lot of times it's not about something being clinically correct or clinically right or exactly right,

it's about how they kind of make people feel.

And I think that definitely is true in technology and it's definitely true in everything that we try to build.

My name is Alexander Way.

I'm the CEO and founder of Scale AI.

Scale AI is the data infrastructure for AI to power the most ambitious AI projects in the world.

Every organization wants to implement AI but oftentimes the biggest bottleneck in their way is being able to create really high-quality data and datasets to power that AI.

At Scale, we sort of view data as the core problem of building great AI whereas a lot of other companies view it as an afterthought and that really prevents AI from having sort of the magnitude of outcomes that it's able to have.

We've raised over $600 million dollars to date,

and we work with everywhere from the largest automakers in the world like Toyota and General Motors to the United States Department of Defense to some of the largest enterprises in the world like Microsoft Square and PayPal,

and some of the leading AI research organizations like OpenAI.

When you learn how to program for the first time, it's kind of shocking, but you actually are generally sort of telling the computer to do very simple things.

The art of programming traditionally is the art of sort of giving computers very black-and-white instructions, very simple instructions that anybody could follow.

And one of the beauties of AI is that you actually have the ability to program computers with judgment and with reasoning and with sort of nuanced understanding of the world.

And so you can have an AI system, look at an image and tell you what's in the image or listen to an audio snippet and understand what's being said.

And it is sort of this incredible enabler for what computers can do or the power of computing.

And in general, I think we've already seen sort of over the past many decades what the power of computers and computing and mobile phones and all that stuff has been on humanity.

And I think AI and machine learning has a huge opportunity to do the same.

Both my parents are physicists and I grew up in the small town in New Mexico called Los Alamos, New Mexico,

where there's a national lab and a lot of the people I grew up with had parents who were scientists of some sort, it was a sort of very special place.

And my mom, from a very young age, taught me about math and physics and science.

And, you know, she taught me with such wonders.

I was really impatient as a kid.

I think I always wanted to be learning more or I always want to be doing more, always wanted to sort of be accomplishing more.

And so, I actually, I left high school after my junior year of high school and then moved out to Silicon Valley to work as a software engineer.

I learned so much about building products, about what it meant to be metrics, focus and data focus and what it meant to build great software.

And then, that's when I was inspired by AI, I sort of saw it in my daily work.

I was like, AI is really cool and I went back to MIT and then for about a year of MIT, I dropped out to start Scale.

We have over 500 people now, so it's pretty insane to watch.

You know, what we originally started as a, you know, a few people in a, in the basement of our investor to what has sort of become.

Where we started was in autonomous vehicles and self-driving.

And I think it was one of the first real use cases and applications of AI that I think caught the imagination of the world.

You know, what if we could have unlimited easy eco-friendly transportation everywhere in the world through autonomous vehicles?

One of the examples that we get really excited about is in health care.

In health care, there's a huge bottleneck in the number of doctors, trained doctors all around the world.

And there's incredible potential for AI and machine learning to actually analyze as many of the cases possible automatically before needing escalation.

So the doctor can spend their time on cases with anomalies or erratic data or whatnot.

And so at Scale, we actually did research with MIT on using AI and machine learning to analyze dermatology data and dermatology imaging to see how AI can actually automate that process and then, therefore, unblock the sort of dr bottleneck.

Another use case that I'm really passionate about is using AI to help solve some of the largest geopolitical problems and working with governments in being able to sort of provide technology to aid in some of these very tough and tricky situations.

In the war with Russia-Ukraine, we actually deployed Scales technology in understanding satellite imagery of major Ukrainian cities, Kharkiv, Kiev and Dnipro to understand what was the amount of damage in key parts of these cities.

And so we analyzed using machine learning as well as satellite and identified all sorts of structures in these cities where there was meaningful damage that wasn't otherwise being addressed or captured by humanitarian efforts.

And so I'm incredibly excited by our work there and actually enabling sort of humanitarian efforts, enabling us to respond to some of the world's most pressing and exigent problems in the world of AI.

There's, I think a lot of very smart people but who are focused, you know, so far out in the future that it's almost unhelpful, you know, there's so many people focused on what's gonna happen when we have AGI or what's gonna happen, you know, two or three decades in the future.

And I think there's not enough people who are really focused on what are the problems that we have today and how can we use artificial intelligence and machine learning to really changed the game today?

And so, I think what's next for us is to be the people, some of the people, hopefully in the world who are focused on how do we solve some of the biggest problem today around climate, around agriculture, around geopolitics, around medicine and really start making an impact, you know, now.