Hey everyone! I'm Alex, CEO/founder of Scale!<p>I just wanted to chime in that we're a YC company as well (S16), and I'm thankful to the HN community for having been supportive through our whole journey.
I saw someone on Twitter post that this was a real life “Not Hotdog” from HBO’s Silicon Valley, though ironically this company doesn’t actually use AI or ML at all it’s just scaled human contract workers.<p>There’s some social commentary in there somewhere.
I don't understand startup valuations well, so would appreciate someone more knowledgeable throwing some light on how these valuations are made. Would I be in the ballpark in assuming that they have a Sales ARR of $125M. At a sales multiple of 8x (for SaaS cos) makes them worth $1B.<p>The $125M is around 12 large customers with contracts of $10M each, which buys them services of 2500 labeling contractors for 2000 hrs/year at $2/hr ($4K/yr).<p>At some point they will stop being a services company which carry a low multiple and switch to automated labeling without contractors (ala self driving cars) or develop some unique IP that they sell as a service?
> It’s built a set of software tools that take a first pass at marking up pictures before handing them off to a network of some 30,000 contract workers, who then perform the finishing touches.<p>Machine learning indeed.
Bit of an AI novice here, I did Norvig's course a few years ago and never worked in the field, but how can a machine take a "first pass" at labelling without being trained? What information is it using to apply labels to the first set of data? How does this approach differ from a conventional classifier? Would the initial guesses essentially be random?
I'm a little confused about what the business model here is. It sounds like they are selling labeled data to companies, and doing this by "label[ing] most of the objects automatically" and then having humans review these labels. So does this mean they are using some unsupervised method to label data, and then selling that to people who want to train supervised models? Why aren't they instead just beating out the people they sell to by solving the same problems without labeled data?
I used to train AI to help researchers find more relevant papers at <a href="http://iris.ai" rel="nofollow">http://iris.ai</a> This was nothing more than just classifying. Would this kind of opportunity be available for data remotaskers at Scale. Best regards for groundbreaking work
So what is your competitive advantage?
I.e. what cannot be replicated?<p>From a technical perspective, can someone just post labelling task to mechanical Turk? what is the difference here?
I really dislike this sort of journalism. Theranos was founded by a 19 year old too. That one didn't work out so well. Was it because the founder was so young? The board so oblivious? (a bit of both if you read the book)<p>What does it really matter how "old" the founder is, does the business have a workable business plan? Can it be profitable? Do people pay enough money for its goods and services to return a net income? Those are interesting questions. That it was started by a teenager is not, to my way of thinking, particularly relevant.<p>I'd much prefer that the article focus on these things which helps us understand the value that they bring to the market and what makes them unique.
In essence, the company pays third worlders a pittance to transfer humanity's skills to the machine. The skill transfer is limited to what can be done with a mouse and screen, but since that's where most human ability is currently manifested, it's hardly a limitation. What happens to the serfs once the transfer is complete? Do they realize they are exchanging temporary wages for eternal futility?<p>I like how the investors rationalized this devil's deal and the usurpation of the poor: "If you could be pulling a rickshaw or labeling data in an air-conditioned internet café, the latter is a better job."