It all started with a breakdancing video.

Now, on the face of it, that shouldn’t have been too surprising, back in 2010. YouTube was already well on its way to assembling a massive collection of the human experience. We had run programs like Life in a Day - asking viewers around the world to submit videos of their daily life on a single day - to document everyday life in a way no-one had parsed before. Breakdancing and hip-hop culture were undeniably a part of that global experience - maybe surprisingly, in countries like France, where communities like Juste Debout had started to document a thriving scene. 

But at YouTube, we had a big problem making sense of those videos. Viewers from around the world had uploaded a huge range of content, with little in the way of titles or tags to make sense of it all. For many uploaders, it was their first time uploading a video to the internet - ever. In spite of that, what made YouTube successful, and what my team focused on, was a vision to drive viewers to videos they didn’t expect, and that hadn’t amassed thousands of views - through related videos, through search, and through trends.

So you can imagine my surprise in 2010 when my team showed me a 15 second video of a man spinning on his back with his sneakers in the air, on a street somewhere in Europe. The video had a random title and the uploader had no followers. Under normal circumstances, this would be a very difficult video for someone to find - just a random selection out of hundreds of thousands of videos that morning. But that day, we tried a new approach to understanding videos, based on what were called Convolutional Neural Networks, with the capacity and training horsepower to work across the whole range of the YouTube catalog.

Sure enough, this time, bubbles appeared next to the video, saying “breakdancing”, “spinning”, even “munchmill” . Trained on tens of millions of videos, deep learning had discovered the connections between this human movement across a few frames of video, and an entire global culture built around hip-hop. 

For me, those connections launched the next decade of my career. The combination of powerful, expressive machine learning models, with new approaches to understanding the diversity and complexity of content shared on the web, was an incredible opportunity to build new experiences. My time launching a startup with Steve Chen highlighted for me how those techniques could be brought to bear to live and immersive content, while my time at Apple helped me to understand how “data centric” AI could also be used to build new hardware and software experiences with a “privacy-first” mindset.

I recently joined Scale to run our engineering organization, in part because of a similar credo that Scale has in our culture - that Ambition Shapes Reality. The next decade of AI achievement is going to be built by teams that take on outsized ambitions, and understand that it is not always a straight line to get there. 

Operational  & Data Centric AI

 The market for AI developer tools and infrastructure is currently small, and limited by the number of companies who are actually AI capable and have teams capable of using ML to drive business goals. But in the long run this market will be an industry-defining set of capabilities, and we have a golden opportunity to own the primitives that build this market, given our leadership position in autonomous vehicles and perception data. Those capabilities will include the ability to drive operational improvements through a counterfactual view of complex systems, being able to leverage multi-modal and Foundation models to work across visual and language data, and building insights in real-time as data distributions shift.

Building out these capabilities for ML teams not only gives us a call option on this really exciting future, but it also gets us early and close access to the small pool of AI practitioners who are pioneering that future today. That partnership between forward-looking researchers and product leaders and engineers characterizes the fastest-moving ML teams today.

Running Through Walls

When I was thinking about joining Scale, I realized I had a lot of vision alignment with Alex, Scale’s CEO. In an internal memo, Alex recently wrote, “I am tremendously excited about what’s beginning to really work here, probably more than I’ve ever been in the history of Scale.” It was obvious to me that Scale is unique, in part due to the massive opportunities in the rapidly growing emergent field of AI, and also in part by the mission upheld by leadership. 

Alex truly understands the nuances of AI. He recently did a TED Talk at Berkeley where he reiterated that AI needs humans to teach it individual values, nudge it to find thoughtful outcomes, and ensure that human intentions and values are aligned with the AI. He talked about how machines make mistakes and how they have to be taught to tell the truth. 

This alignment was a grounding factor for me. Alex understands that “AI is a supercharger for humans - when AI is better than humans, it makes humans better. AI will enable us to be even more creative and more idea driven. It allows us to embrace the generative aspect of human nature so we can run faster with our ideas and build better, more powerful solutions to the world’s biggest problems”. And building solutions to the world’s biggest problems is a focal point of Scale’s culture and formed the company’s Credos.

Our credos provide a framework for us to make decisions and work effectively as a team. The one that has resonated with me the most is “run through walls.’ We are undaunted by barriers and race towards overcoming them. We step outside of our comfort zone, test our ideas, and do the hard work to get to the right solution to a hard problem. I have seen this credo in action with every one of my team members. 

Become a Scalien!

Scale is in an exciting place where growth opportunities for AI organically grow your career too. Being at Scale allows you to work with high visibility customers - often impacting millions of users.

You’d have the opportunity to ideate with some of the world’s leading ML and Data Science teams at our partner companies, and find creative ways to adapt new ML techniques like Generative Models, Diffusion Models,  and Large Language Models, or Online Optimization techniques applied to expressive embedding spaces, to building a new product.

It’s been a long and rewarding journey since that first breakdancing video. I know AI and its innovation will continue to surprise me and I am excited to experience that here at Scale. I hope that you will join me here too.