
The First Wave of Corporate AI Is Doomed to Fail - YeGoblynQueenne
https://hbr.org/2017/04/the-first-wave-of-corporate-ai-is-doomed-to-fail
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dave_sullivan
TLDR "AI will transform industries, it hasn't happened yet, some companies
have given up because it's too hard, some companies haven't, mobile apps are
cheap and so too will AI be."

This is the best business model for AI startups right now: Raise money (at
least $10m) to start a private equity fund. Now find businesses that YOU know
will massively benefit from AI but their management simply doesn't understand.
Buy the company. Use AI to increase margins. Sell the company. Profit.

This is a massively better idea than selling your time for X to companies run
by people who don't understand what you'd be doing for them.

Actually, there's a bit more to it, but that's where the next wave of AI
profits will come from (PE funds, not startups and not VC).

Speaking of which, if anyone is doing something similar, feel free to contact
me.

~~~
tim333
The subset of people who can raise >$10m for a private equity fund and that
understand current AI is probably quite small.

~~~
dave_sullivan
It's a pretty open strategy at this point. In a few years, everyone will be
doing it.

Also, PE itself is such a messed up industry. Very conservative, hasn't
changed their playbook in 2 decades. The guys at KKR aren't the ones that are
going to figure this out.

~~~
tim333
Maybe a PE expert should partner with an AI expert. They seem rather different
skill sets.

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ecopoesis
Except: early AI has already succeeded. Things like Siri and Alexa work
everyday for millions of people. Then there's also the hidden AIs we so
ingrained in our life we don't notice like Google's searching or even credit
card fraud detection.

The AI industry has a terrible problem: once we create an algorithm for
something it's no longer AI, it's just an algorithm.

It feels like there should be a corollary to Arthur C Clarke's famous law: Any
sufficiently advanced algorithm is indistinguishable from intelligence.

~~~
scarmig
At this point, it's clear that the only version of AI that'll be acknowledged
by the hard skeptics is "a system that thinks and works exactly like the best
humans in history along every single individual axis, except a kabillion times
better."

~~~
woah
Well, that's what AI is always hyped as, by its proponents, and its detractors
who want us to beware of a singularity where godlike machine intelligences
take all the jobs and then turn the entire universe into paper clips.

After being fed a diet of daily articles about the immense power of AI, it's
pretty natural for someone to look at a program which gets your spoken song
and movie requests right 75% of the time and say "that's not AI".

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arcanus
I actually think this misses the biggest reason that this AI cycle could fail.
A great deal of machine learning talent is concentrated in big companies right
now, because of the nice salaries and stability it provides research
initiatives.

But the real disruptive applications of AI will necessarily apply to new,
untapped markets. It won't fit into search or ad markets, for instance.

Until AI moves out of research at labs and bigco into the startup space, it
will be necessarily limited in scope.

Interesting because it could be the failure of this cycle that 'releases'
talent into a startup ecosystem that lays the seeds for the distruptive next
phase.

~~~
curun1r
> Until AI moves out of research at labs and bigco into the startup space

Sounds like what OpenAI
([https://openai.com/about/](https://openai.com/about/)) is trying to achieve.

~~~
arcanus
Somewhat. As I see it, openAI is trying to build the tools (and research
papers, IP, etc.) in a publicly facing venue that could power the frameworks
that ultimately power the disruptive wave of startups.

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sk5t
This article reads as rather lazily-written. Consider:

 _Allstate car insurance already allows users to take photos of auto damage
and settle their claims on a mobile app. Technology that’s been trained on
photos from past claims can accurately estimate the extent of the damage and
automate the whole process._

How do we make the jump from very well-established (even boring) use of tech--
sharing photos--to computer vision and analysis that can value a vehicle,
understand the damage both visible and concealed, and assign useful figures
for both repair and value? And won't such results be disputed at an
astonishing rate? I mean, sure, it's a nice idea, but there does not seem to
be any bridge at all from one concept to the other.

 _The good news is that emerging marketplaces for AI algorithms and datasets,
such as Algorithmia and the Google-owned Kaggle, coupled with scalable, cloud-
based infrastructure that is custom-built for artificial intelligence, are
lowering barriers._

Renting someone else's algorithm sounds like a very risky proposition, as they
can easily drive you out of the market when it becomes worthwhile to do so
(see also Amazon's strategy around fungible third-party items that are
profitable).

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scarmig
Was it just me, or did the title have little to do with the actual article?

Everything written except the title seems pretty reasonable to me, and I'm an
AI optimist.

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FullMtlAlcoholc
Yeah, those spam filters sure are a big failure all right.

/s

