
Identifying a SaaS Market That Machine Learning Can Address - gk1
http://tomtunguz.com/ml-cost-reduction/
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BjoernKW
I'd say that by far the greatest potential in process optimization still lies
with improvements in areas that don't require machine learning, such as:

\- fully or partially paper-based processes

\- digital systems that can't easily exchange data with one another

It always makes me want to cry when I sees systems where due to the lack of a
proper interface between software components the API between these components
essentially can be described as "Print it out on paper and hand it to someone
else to type it into another software."

You don't need machine learning for that. You need APIs and first and foremost
you need people to talk to each other and agree upon joint requirements for
different components and the system as a whole.

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pwaai
chances are that if they haven't automated it, it's not revenue generating or
important enough to invest capital to fix it

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BjoernKW
Could be but then machine learning doesn't help either.

There's still a lot to fix and automate though with a significant ROI.

Did you know for example that the paperwork required for sending a shipping
container from Shanghai to Rotterdam is more expensive than the actual
transport of the container itself?

Freight forwarder Maersk considers this to be a painful enough problem to try
and solve it with Blockchain technology now (one of the few cases where using
a Blockchain actually makes sense because not all participants involved in the
process necessarily trust each other).

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pwaai
> Consequently, if you looking to build a machine learning based SaaS company,
> find a really expensive internal process and automate it.

so basically the "how to" is "find a problem and improve it"

this falls short of the expectation I had going into the article...hoping this
was some sort of framework to identify markets

I feel like this is clickbait

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AznHisoka
Most of Tunguz’s posts are like this. His analysis of SaaS startups are very
meta and a few sound like a armchair quarterback. His style is not for
everyone and if you are an entrepreur, his advice is rarely practical and
specific.

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rdlecler1
TL;DR “find a really expensive internal process and automate it.”

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qaq
Do you need AI for that ?

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infinite8s
No but you can charge more if you call it AI.

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Terretta
The snazzy misnomer of the day is “robotics”, short for the more accurate
“robotic process automation” or “RPA”:

[https://en.m.wikipedia.org/wiki/Robotic_process_automation](https://en.m.wikipedia.org/wiki/Robotic_process_automation)

// Note: If you’re interested in getting paid to drive automation efficiencies
at massive scale with any of RPA, AI, ML, whatever, I’m hiring.

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thanatropism
We'd been hearing about this "robotization". At some point our team leader
talked in depth with some prospects and brought back the good news -- hey,
this is what we were trying to sell all along.

So now we're robot makers apparently.

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brucephillips
> To date, most software imbued with machine learning reduces costs rather
> than increase revenues.

Source?

ML includes trading algorithms, recommendation systems, self driving cars,
search, translation, asset pricing, etc., all of which are revenue generating.

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mi100hael
The point is that all of those processes were already being done, just in a
less efficient manner.

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brucephillips
That can be said of any product, not just ML.

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nicodjimenez
This article is nonsense. Grammerly or Amazon Echo are examples of how AI can
do _new_ things in a frictionless way. Reducing friction is a huge use case
for machine learning. Lowering costs in this splintered economy of haves and
have nots is certainly valuable but hardly the most interesting or valuable
opportunity for ML.

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Eridrus
I think people, including the author, vastly underestimate what will happen
when things get cheaper. I think smart speakers are still a case of
transcription/understanding being cheap enough that we can do it for trivial
things like turning on the lights.

But I think ML is even more powerful, in situations where we learn from real
world data rather than some human/programmatic data labeling, we can exceed
the abilities of people. Google's recent ML for health stuff comes to mind
where it's much better at humans at certain tasks.

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dboreham
With Machine Learning, obviously!

Meta Machine Learning.

