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Hi friends đź‘‹ ,
Happy Monday! Hot Vax Summer is in full-swing, and while all of you are out trying to remember how to party, I’m sitting in a basement doing my darndest to expose myself as a fraud.
My job is to go out to the edges and try to explain things that seem crazy and complex, first to myself and to all of you in the process. I’m not writing as an expert, but as someone who’s taking you along on my own exploration. I’ll get some things wrong, and you’ll call me out, and hopefully we all get a little smarter. In the process, two overarching themes for Not Boring have emerged:
- Everything is going to be crazier than we can imagine. Things seem wild right now, but if history is any indication, this period is going to look quaint in a decade and antique in a century. We need to be prepared.
- There are still business and strategy principles that underpin even the craziest seeming ideas and businesses. Web3 is really about network effects. Zero-knowledge proofs are about long-standing consumer preferences for convenience and privacy. Even the most futuristic-seeming tech can become commoditized in a handful of years; the businesses built on them still need moats.
Today’s essay is about Scale AI, a company that started with a non-obvious wedge into a large market that’s growing and evolving rapidly, and that will one day grow and evolve faster than humans can comprehend. It’s a company you should know about:
- At just five years old, Scale was recently valued at $7.3 billion.
- It’s (actually) like Stripe: leveraging a seeming commodity -- data labeling -- as a wedge into a key position in the rapidly growing and evolving AI and ML space.
- By building infrastructure, Scale has a chance to help grow AI/ML usage from only 8% of companies today, to all companies within the next two decades.
I’m certainly not an AI/ML expert. I will certainly get some things wrong and welcome feedback. I’m just taking you along on my learning and diligence.
A note: Not Boring occupies a unique and tricky position. I write about the ideas and companies that fascinate me most and best fit the themes above, but I don’t want to sit passively and watch, so I often invest in the companies I see as best-positioned to bring about and capture value from the trends, too. If I were a journalist, this would be frowned upon. But I’m not. I’m just trying to dive in as deeply as I can to understand all of this better, bring back what I learn, expose my thought process (and potential conflicts) openly, and help shape the future in a small way.
Full disclosure: I’m an investor in Scale, but didn’t receive compensation from Scale for this piece.
Let’s get to it.
^^ Click to jump straight to the web version ^^
On Thanksgiving last year, Alexandr Wang posted his first essay to Substack. The essay, Hire People Who Give a Shit, was good. So was the next one, Information Compression, which he wrote on December 5th. Both provided well-reasoned glimpses into the way Wang runs his company day-to-day. But the essays weren’t the most illuminating thing about the Substack. The name he chose for it was:
Rational in the Fullness of Time
The name is fitting given Wang’s long-term focus at his company, Scale. Scale has spent its first half-decade focused on the unassuming first step of the machine learning (ML) development lifecycle -- data labeling or annotation -- with a belief that data is the fundamental building block in ML and artificial intelligence (AI). For a while, that has meant that the company has looked like a commodity services business.
Scale exists to “accelerate the development of AI applications” by “building the most data-centric infrastructure platform.” Its core belief, and the assumption on top of which the business is built, is that “Data is the New Code.”
Scale wants to be to AI what AWS is to the cloud, Stripe is to payments, Twilio is to communications, or Snowflake is to Data Analytics.
Now, of course Scale wants to be like those companies. Who doesn’t? But it’s on a credible path:
- Scale spent its first four years focused on annotating data for use in AI/ML models. Now, it’s expanding downstream to develop the models itself and eat other pieces of the AI/ML value chain.
- It surpassed $100 million Annual Recurring Revenue (ARR) in 2020, just its fourth year in operations, and continues to more than double year-over-year.
- Its clients range from the US Department of Defense to PayPal to all of the major Autonomous Vehicle (AV) companies and largest tech companies.
- Just five years old, it’s valued at $7.3 billion after a recent $325 million funding round co-led by a triumvirate of top growth investors Greenoaks Capital, Dragoneer, and Tiger Global.
- It brought on former Amazon exec Jeff Wilke as a special advisor to the CEO.
- Scale builds infrastructure, which looks pretty unsexy, until it doesn’t.
In a decade, if Scale is successful, any company that wants to build something using AI or ML will just stitch together five different Scale services like they stitch together AWS services to build something online today. Scale could collect or generate data for you, label it, train the machine learning model, test it, tell you when there’s a problem, continue to feed it fresh, label data, and on and on. Via Scale APIs, companies of any size will be able to build AI-powered products by writing a few lines of code.
Take a second to appreciate that: within a decade, AI, long the stuff of sci-fi writers’ imaginations, will be as easy to implement as accepting a credit card is today. That’s mind-blowing.
But that’s in the future. First, data.
When Wang and co-founder Lucy Guo founded Scale out of Y Combinator in 2016, the company was called Scale API and its value prop was essentially that it was a more reliable Mechanical Turk with an API. They started with the least sexy-sounding piece of an incredibly sexy-sounding industry: human-powered data labeling.
Customers sent Scale data, and Scale worked with teams of contractors around the world to label it. Customers send Scale pictures, videos, and Lidar point clouds, and Scale’s software-human teams would send back files saying “that’s a tree, that’s a person, that’s a stop light, that’s a pothole.”
By using ML to identify the easy stuff first and routing more difficult requests to the right contractors, Scale could provide more accurate data more cheaply than competitors. Useful, certainly, but it’s hard to see how a business like that … scales. (I’m sorry, but I also can’t promise that will be the last scale pun).
Scale’s ambitions are obfuscated by its starting point: using humans to build a seemingly commodity product. A bet on Scale is a bet that data labeling is the right starting point to deliver the entire suite of AI infrastructure products.
If Wang is right, if data is the new code, the biggest bottleneck for AI/ML development, and the right insertion point into the ML lifecycle, then the brilliance of the strategy will unfold, slowly at first then quickly, over the coming years. It will all look rational in the fullness of time.
Scale has a high ceiling. It has the potential to be one of the largest technology companies of this generation, and to usher in an era of technology development so rapid that it’s hard to comprehend from our current vantage point. But it hasn’t been all clear skies to date, and the future won’t be easy either. It will face competition from the richest companies and smartest people in the world. It still has a lot to prove.
In either case, Scale is a company you need to know. It’s also an excellent excuse to dive into the AI and ML landscape and separate fact from science fiction. It’s looking increasingly likely that AI will find itself in the technology impact pantheon alongside the computer, the internet, and potentially web3.
The combination of all of those technologies will change the world in unpredictable ways, but one thing’s certain: the world only gets crazier. We’re at an inflection point, so let’s get ready by studying:
- The State of AI and ML
- Getting to Scale.
- Scaling Like Stripe.
- The Bear Case for Scale.
- The Bull Case for Scale.
- Scale’s Compounding Vision.
Before we get to Scale, though, we need to get on the same page with what AI and ML are.
The State of AI and ML
I’ll start with the punchline, and then get to the joke: whether you call it AI or ML, it’s useless without good data.
Now the joke. There’s a set of jokes among technical people whose premise is that machine learning is just a fancy way of saying linear regression and that artificial intelligence is just a fancy way of saying machine learning. No one made the joke better than this guy:
The idea is that machine learning is the real thing, written in the Python programming language, and AI is just hype-y way of saying ML that people use to fundraise.
There are differences, though. I asked my friend Ben Rollert, the CEO of Composer and my smartest data scientist friend, how he would define the differences between AI and ML. His response seems pretty representative of the general conversation:
AI is a broad bucket of “algorithms that mimic the intelligence of humans,” some of which exist today in machine learning and deep learning, and some of which still live only in the realm of sci-fi.
Source: IBM
AI is split broadly into two groups: Artificial Narrow intelligence (ANI) and Artificial General Intelligence (AGI). When we talk about AI applications today, we’re talking about ANI, or “weak” AI, which are algorithms that can outperform humans in a very specific subset of tasks, like playing chess or folding proteins. AGI, or “strong” AI, refers to the ability of a machine to learn or understand anything that a human can. This is the stuff of movies, like the voice assistant Samantha in Her, the Agents in The Matrix, or Ava in Ex Machina.
Most of the things that we call AI today fit into the subset of AI known as machine learning. ML, according to Ben, is:
Code that is learned from data instead of written by humans. It’s inductive instead of deductive. Normally, a human writes code that takes data as input. ML takes data as input and lets the machine learn the code. In ML, algorithm = f(data).
ML has been around since the 1990s, but over the past decade, a subfield within ML called deep learning has ignited ML and AI application development. According to Andrew Ng, the founder of Coursera and Google Brain, deep learning uses brain simulations, called artificial neural networks, to “make learning algorithms much better and easier to use” and “make revolutionary advances in machine learning and AI.”
In a 2015 talk, Ng said that the revolutionary thing about deep learning is that it, “Is the first class of algorithms … that is scalable. Performance just keeps getting better as you feed them more data.”
Major improvements in ML and AI seem to come from step function changes in the amount of data a model can ingest.
In 2017, researchers at Google and the University of Toronto developed a new type of neural network called Transformers, which can be parallelized in ways that previous neural networks couldn’t, allowing them to handle significantly more data. Recent advancements in AI/ML like OpenAI’s GPT-3, which can write longform text given a prompt, or DeepMind’s AlphaFold 2, which solved the decades old protein-folding problem, use Transformers to do so. GPT-3 has the capacity for 175 billion machine learning parameters.
These advancements have led to a renaissance in the applications of AI and ML. I asked Twitter for some recent examples, and many of them are truly mind-blowing:
For all of the technological advancements, though, it all comes down to data. In his 2015 Extract Data Conference speech, Ng included this slide that highlights the benefit of deep learning:
What deep learning solved, and Transformers expanded on, is allowing models to continue to scale performance with more data. The question then becomes: how do you get more good data?
Getting to Scale
Scale has all the stuff that Silicon Valley darlings are made of: acronyms like AI, API, and YC, huge ambitions, young, brilliant college dropout founders, and an insight born of personal experience: AI needed more and better data.
Alexandr Wang was born in 1997 in Los Alamos, New Mexico, the son of two physicists at Los Alamos National Lab. Alexandr is spelled sans second “e” because his parents wanted his name to have eight letters for good luck. Whether through luck or genetics, Wang was gifted. He attended MIT, where he received a perfect 5.0 in a courseload full of demanding graduate coursework, before dropping out after freshman year. Wang worked at tech companies Addepar and Quora, and did a brief stint at Hudson River Trading.
In 2016, Wang (then 19) joined forces with Lucy Guo (then 21), a fellow college dropout (Carnegie Mellon) and a Thiel Fellow, and entered Y Combinator’s Spring 2016 batch. They didn’t quite know what they were going to build when they entered, but Wang, like so many founders, hit upon a problem through personal experience. He told Business of Business that at MIT:
There was nobody building anything with AI, despite the fact that there were hundreds of students at MIT, all brilliant, very hardworking people. We're all studying AI. And when I dug into it, I realized that the data was the big bottleneck for a lot of these people to build meaningful AI. It took a lot of time and resources to add intelligence to data, to make it usable for machine learning. There were no standardized tools or infrastructure, there was no AWS, or Stripe, or Twilio to solve this problem. I even discovered it firsthand, because I wanted to build a camera inside my fridge, so it could tell me when to refill my groceries and what I need to buy. Even for that I just didn't have any of the data to make it work.
So Wang and Guo built Scale during YC, and launched it at the end of the program, in June 2016. Before it was Scale AI, they called it Scale API.
If Scale’s ambitions were as large then as they are now, the co-founders hid that ambitious light under a bushel. At the time, the value prop they pitched was clear: API for Human Tasks.
While there was a lot of hype around what AI might do, the fact remained that there were many tasks, even repetitive and seemingly-simple ones, for which humans were much better suited. On the first version of the website, uncovered by the Internet Archive’s Wayback Machine, Scale listed three examples:
At this point, Scale billed itself as a more reliable, high-quality version of Amazon’s Mechanical Turk, which used APIs to simplify the process of requesting work, and vetted, trained people on the backend, with a peer-review system, to ensure quality outputs. Instead of hiring internal teams of people to review content on a social media site, for example, a company could feed content through Scale’s API to teams of people trained to decide whether something someone wrote was against the site’s terms of service.
This 2016 Software Daily podcast with Wang and Guo was a good representation of the way they described the business then:
In that interview, they hinted that the product had moved beyond just content moderation and data extraction to what would become their calling card: data annotation. That same month, image recognition showed up on the website as a use case for the first time.
For the first year, Scale seems to have survived on the $120k that YC invests in exchange for 7% of the company. Then, in July 2017, it announced a $4.5 million Series A led by Accel.
Scale’s Evolution as Told Through Funding Announcements
Funding is not the most important thing for a startup, but the history of Scale’s funding announcements tell the story of the evolution in its focus and the way it described itself.
Already, in the Series A announcement, Scale unveiled its larger ambitions, not just a better Mechanical Turk, but a better API for training data. The announcement focused squarely on AI, mentioning it seven times:
Our customers agree that integrating AI with accurate human intelligence is crucial to building reliable AI technology. As a result, we believe Scale will be a foundation for the next wave of development in AI.
If the 2017 Series A highlighted the evolution from API to AI (it would change its website from scaleapi.com to scale.ai in February 2018), the August 2018 announcement of Scale’s $18 million, Index-led Series B was about its role in data labeling for autonomous driving. The post highlighted that:
In autonomous driving, one of the most prominent applications of deep learning today, Scale has become the industry standard for labeling data. We’ve partnered with many industry leaders such as GM, Cruise, Lyft, Zoox, and nuTonomy. We’ve labeled more than 200,000 miles of self-driving data (about the distance to the moon).
At this point, it’s worth pausing to describe just what data labeling is.
Data Labeling: Hot Dog, Not Hot Dog
As we covered above, ML models take data as an input and let the machine learn its way into figuring out the right code. Without data, there is no ML or AI, and bad or mislabeled data is worse than no data at all. “Garbage in, garbage out.”
To get data to feed the models, companies can either use open data sets, buy data, or generate the data themselves. Scale’s largest customer segment to date, companies building autonomous driving technology, generate petabytes of data as they drive around and capture video and Lidar point clouds of their surroundings. In their raw form, those videos don’t mean anything to the models that make decisions about when to stop, go, swerve, speed up, or slow down. So companies like Toyota and Lyft send huge files to Scale, and Scale’s job is to send the files back labeled, or annotated.
Source: Scale PandaSet
Scale’s system is a “Human-in-the-Loop” (HIL) system. From a cost and speed perspective, the ideal would be to have algorithms tag everything, but the algorithms aren’t good enough yet (and certainly weren’t good enough when Scale launched in 2016). This scene from Silicon Valley captures it well:
Source: BVP State of Cloud with Packy Scale Addition
Scale Nucleus
The ML Lifecycle, Scale
Scale Document
Source:The Generalist
Source: Matt Turck
