
Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B Valuation - cristinacordova
https://www.bloomberg.com/news/articles/2019-08-05/scale-ai-is-silicon-valley-s-latest-unicorn
======
ayw
Hey everyone! I'm Alex, CEO/founder of Scale!

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.

~~~
ArtWomb
Congrats! Two quick questions...

Is it weird sharing the same name as a fashion icon ;)

And I'm curious about your ML "stack". Particularly the chicken and egg
problem. Are you using something like Tensorflow with pre-trained binaries,
perhaps from a vendor? Or is it 100% proprietary. Thanks!

~~~
ayw
Re 1—It has been a bit of annoyance growing up (for example, Google
autocorrects "Alexandr Wang" to "Alexander Wang"), but we run different
circles ;)

Re 2—As with most companies working on ML these days, our stack is not fully
proprietary. We don't take too strong an opinion on ML framework and use both
Tensorflow and Pytorch currently. We generally use neural network
architectures from the literature and then iterate on top of them to suit our
unique problem requirements.

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tempsy
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.

There’s some social commentary in there somewhere.

~~~
ayw
We _do_ use AI and ML to help making the labeling process more efficient, but
you are correct we do have scaled human insight that ensures very high
quality.

One difference from "Not Hotdog" is that our data is used to power the
algorithms of other AI/ML companies like OpenAI, Waymo, Lyft, etc., so it's
imperative that we have impeccable quality. That necessitates humans to ensure
accuracy, particularly in safety-critical applications like self-driving cars.

~~~
tomrod
I consider this a good thing. I hope the next few years play out well for your
company. Good luck!

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vadym909
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.

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).

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?

~~~
choppaface
One major risk is that their customers simply build teams in-house. Microsoft
has had a very large team for years (and MSR / Ofer Dekel has actually
published a lot of useful research on how to handle “crowdsourced” labels).
Companies have been building productive off-shore labeling / moderation teams
since the early days of Crowdflower. At some point, it’s not just the cost
that makes sense, but rather the Product team wants a reliable workforce that
they can control.

Another risk is that the well-funded self-driving customers go belly-up.
However, one important facet is that dead players don’t release much data.
MobilEye has a vast dataset (including images from not just Tesla but other
automakers) but that data isn’t going anywhere. Neither is Nvidia’s 180PB of
HD recordings. (Release or transfer in part requires dealing with PII of the
people in the recordings. Now if only the offshore labelers weren’t handed PII
for free...).

The valuation is likely a forward-looking bet on AI as whole versus the
current suite of contracts. Anybody using an off-the-shelf model will want
some labels after their first proof of concept. I wouldn’t argue that the math
makes sense but rather that demand does look underserved.

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pkaye
> 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.

Machine learning indeed.

~~~
ayw
To be clear, there is _real_ machine learning that makes the labeling more
efficient.

You can see some videos of what this looks like in this Twitter thread:
[https://twitter.com/BW/status/1158407524216909826](https://twitter.com/BW/status/1158407524216909826)

~~~
typon
Very similar to the Magic Wand tool in Photoshop which gives a good starting
point and can be improved on manually in the problem areas where colors are
ambiguous.

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scalrname
Congrats on the recent purchase of scale.com. Can you share how much was the
domain cost or some words about this acquisition?

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kareemsabri
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?

~~~
plinkplonk
The business model is, initially, selling human labeling services to owners of
data (like Uber or Google), using third world cheap labor to keep costs low.
This is very much like a call center service.

Once a sufficiently large corpus of human labeled data is available (across
clients and datasets probably), that labeled data is used to train a 'first
pass' labeling system.

It then becomes a virtuous cycle. Now the labeling is done in two phases. The
first pass system makes its best guess, which is then reviewed by the existing
human work force. Over time the first pass labeler gets better and better,
till only very tricky/borderline cases need human intervention.

The end game is anyone's guess. Pretty clever biz model hack.

~~~
vadym909
like Google Search using human raters to verify their updated algorithms
improve results

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khannate
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?

~~~
fnbr
Presumably they have a supervised model that they've trained on all their
labeled data so far (possibly pooled across clients). They'd use this to
estimate labels for their data, and then have humans correct it. They're
basically doing the standard supervised data training loop.

If I had to guess, the long term plan probably is to move up the stack and
sell the models to their clients.

------
jonas_kgomo
I used to train AI to help researchers find more relevant papers at
[http://iris.ai](http://iris.ai) This was nothing more than just classifying.
Would this kind of opportunity be available for data remotaskers at Scale.
Best regards for groundbreaking work

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swampthinker
Hey @ajw, congrats on the growth. Quick question for you: How does Scale's
labeling speed and quality compare to Hive?

~~~
ayw
We have many clients who have switched from Hive. There’s usually a step
change improvement in quality and scalability—up to 10x improvement in error
rates.

------
sindergirl
Reminds of Sleep Delaer:
[https://en.wikipedia.org/wiki/Sleep_Dealer](https://en.wikipedia.org/wiki/Sleep_Dealer)

------
streetcat1
So what is your competitive advantage? I.e. what cannot be replicated?

From a technical perspective, can someone just post labelling task to
mechanical Turk? what is the difference here?

~~~
nikanj
Today: The $100M war chest that lets them undercut the prices of mechanical
turk

Tomorrow (Maybe): Huge corpus of previously solved cases by humans, that can
be used to train a custom model to replace the humans

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troquerre
Congrats Alex! It's been amazing watching your journey from the first pivot to
Scale to now.

~~~
MetalGuru
What was the first pivot?

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yters
Human powered AI for the future! Because artificial intelligence is not.

------
scottrogers86
whats made you more successful than other players in this space?

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ChuckMcM
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)

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.

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.

~~~
minimaxir
Incidentally this frame is what got the most upvotes, compared to more neutral
framing of the new valuation.

[3 points] Scale AI (YC S16) raises $100M at $1B+ valuation to go beyond AI
data labeling:
[https://news.ycombinator.com/item?id=20615657](https://news.ycombinator.com/item?id=20615657)

[11 points] Scale (YC S16) Raises $100M from Accel and Founders Fund at $1B
Valuation:
[https://news.ycombinator.com/item?id=20614672](https://news.ycombinator.com/item?id=20614672)

~~~
dang
We merged this thread into the latter, which was posted earlier and has the
less baity title.

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sindergirl
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?

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."

~~~
DoreenMichele
I'm an American who began doing online gig work (for a different company, not
Scale) while homeless. It allowed me to pay down debt and get back into
housing under circumstances where a normal job was out of the question.

This worked in part because my income was portable, so I was able to take a
train to a more affordable area to get a place within my limited budget. This
ability to move at will and take my income with me was historically largely
limited to the Jet Set and comfortably well-off retirees.

For many people, doing gig work is a tremendous opportunity with a very big
upside. It can be a huge improvement in both their standard of living and
quality of life.

Most people decrying such labor arrangements aren't doing anything whatsoever
to offer a better alternative. Color me unimpressed.

