
Does AI make strong tech companies stronger? - mkbkn
https://www.ben-evans.com/benedictevans/2018/12/19/does-ai-make-strong-tech-companies-stronger/
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fullshark
So basically yes despite this author’s claim it will decentralize. It will
make market leaders in any field stronger / less threatened as they have the
relevant proprietary data.

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AznHisoka
This is why I vehemently disagree with anyone that claims creating a startup
is easier today than in the past.

Not only are the low hanging ideas saturated (ie website monitoring, brand
monitoring, fitness apps), but the bigger ideas require a lot of data, and the
big tech companies have the clear advantage.

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dangoldin
I think creating a small/lifestyle startup is easier than ever since
distribution/globalization is much easier but creating the next big thing is
much more difficult.

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mattmg83
That's the best phrasing I've read that reconciles these two beliefs (easier
to build + saturation). Nicely and simply put.

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2sk21
I think Ben Evans is a lot more optimistic than I am about about how feasible
to train good models from data. By the time you have considered all the
caveats, the number of candidates for applying ML greatly decreases.

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arcanus
I don't disagree that the number of applications of ML/AI is not as universal
as the hype would lead one to believe at present.

However, don't underestimate that AI/ML could be a secular growth over twenty+
years. From that perspective, marginal (but constant) growth could result from
widespread adoption of these methods (with tweaks) to new markets.

A VC should necessarily take a relatively long perspective,so your two
viewpoints are not necessarily irreconcilable.

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evrydayhustling
Lots of good, accessible points here, but I think it's easy to overestimate
the moat around [business] data. [Google] data includes pretty much every
aspect of human activity, since they keep their platform in your pocket, in
smart home devices, and somewhere in the path of so many online intents.
Chinese surveillance and shared platform data is a similar asset - both are
everything-adjacent for a massive population.

And that's the big guys. A couple of years ago, this article might have said
that MasterCard and Visa have all the spending data... But then Paribus proved
that a scrappy startup with a free service could get tens of millions of
people to share all their online receipts in record time, and give Capital One
a great way of catching up through acquisition. That's not an equivalent
dataset, but it's good enough for a lot of applications.

I definitely think proprietary data exists, and that companies will benefit be
exploiting it with ML. But they should be very careful about assuming their
data will uniquely cover an industry, or even a wide swathe of applications,
for long. And they might not have to simply leverage ML, but actually
reorganize their business around what can remain unique about their data (like
they do about every other asset).

And for VCs evaluating new data-oriented startups, I wonder if they will need
new thinking about the time horizon on which investment pays off. Once an
application for a new data asset proves valuable, it may turn out to be much
more replicable than expected.

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lifeisstillgood
>>> but actually reorganize their business around what can remain unique about
their data (like they do about every other asset).

this.

I am advocating the idea of a "programmable company" that is the end point of
automation - where once you find product market fit the rest is automated -
perhaps a better phrase is market / data fit

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polskibus
It's not AI itself but data and hardware tech they don't share (freely or
commercially).

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tybit
That’s what they’re saying, just like sql, ML will be accessible to everyone
and it’s having the data to make use of it that will differentiates companies.

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RA_Fisher
I enjoyed the article but I see two overlooked aspects that significantly
weaken the author's argument:

1\. Statisticians are substitutes for data. You don't necessarily need new /
more data if you have a statistician.

2\. Data often contains a lot of redundant information. Big data may simply be
duplicative.

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maxtollenaar
Could you elaborate more on the first point? I don't really get it

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nradov
Humans can often learn new things from many fewer data points than current ML
algorithms. Sometimes a single data point suffices.

Human statisticians can apply a variety of mathematical tools to fit different
situations. ML systems tend to be more like one-trick ponies.

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maxtollenaar
I’m not sure I could agree with that statistician can “learning new things”
with smaller data points. Statisticans might come and see the pattern with
“better” prior than ML models, that allow them to come up with better
conclusion. However, given the same dataset, there is a maximum to thee
information that can be extracted from the dataset. Ideally, Any human(think
of human brain as a pattern recognizer) or appropirate statistical method
would come up with the same information from the datast, given no prior

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RA_Fisher
Amazingly there is no upper limit on information that can be extracted. :-)
Also, by applying increasingly sophisticated techniques, statisticians can
extract increasing amounts of information from the same data. Don't have data?
Just add a statistician.

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maxtollenaar
I’m not sure if you’re trolling or not, cus there is definitely an upper limit
to how information can be encoded in data.

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momentmaker
It will be interesting to see if the strong tech companies are willing to sell
their data via API or just safe guard it for their own use.

