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Ask HN: Why do so many startups claim machine learning is their long game?

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Ask HN: Why do so many startups claim machine learning is their long game?

#1

Post by crtlaltdel »

I work with and speak to many startups. When I ask questions around the product value, especially in context of defensibility, they assert that their "long term play is using machine learning on our data". This has been pretty consistent in the last few years, regardless of the nature of the product or the market for which the product is targeted. This typically comes with assertions such as "data is the new oil" and "once we have our dataset and models the Big Tech shops will have no choice but to acquire us".

To me this feels a lot like the claims made by startups I've encountered in past tech-hype-cycles, such as IoT and blockchain. In both of these areas there seemed to be a pervasive sense of "if we build it, they will acquire".

The question I have for HN is in two parts:

1. Why is it that a lot of startups seem to be betting the farm, so to speak, on "machine learning" as their core value?

2. Is it reasonable to be highly skeptical of startups that make these claims, seeing it as a sign they have no real vision for their product?

[EDIT]: add missing pt2 of my question

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#2

Post by smacktoward »

You forgot the second part :-D

I think the answer is pretty simple: it sounds good, and it's hard to challenge. It's essentially a promise that they will invent a black box with magic inside. Since nobody can see inside the black box, it's hard to argue that there isn't actually magic in it.

The long-term problem, of course, is that there aren't that many actual magicians in the world. Most of the people who bill themselves as magicians are either people who just think they're magicians, or people who know they aren't but don't mind lying about it.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#3

Post by crtlaltdel »

smacktoward wrote: post #2

You forgot the second part :-D I think the answer is pretty simple: it sounds good, and it's hard to challenge. It's essentially a promise that they will invent a black box with magic inside. Since nobody can see inside the black box, it's hard to argue that there isn't actually magic in it. The long-term problem, of course, is that there aren't that many actual magicians in the world. Most of the people who bill the…

Thanks! I have edited my post to account for my missing part2.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#4

Post by tlb »

There are surely some startups for which this is bullshit. But the good version of it is:

- take some valuable task that's never been successfully automated before

- do it manually (and expensively) for a while to acquire data

- build an automated system with some combination of regular software and ML models trained on the data

- now you can do a valuable task for free

- scale up and profit

The risk is that it's hard to guess how much data you'll need to train an accurate, automated model. Maybe it's very large, and you can't keep doing it manually long enough to get there. Maybe it's very small and lots of companies will automate the same task and you won't have any advantage.

I think there'll be some big successes with this model, and many failures. So be skeptical -- ML isn't a magic bullet. But if a team has a good handle on how they're going to automate something valuable, it can be a good bet.

As an investor, you may well face the situation down the line "We've burned through $10M doing it manually, and we're sure that with another $10M we can finish the automation." Then you have to make a hard decision. With some applications like self-driving cars, it might be $10B.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#6

Post by buboard »

tlb wrote: post #4

There are surely some startups for which this is bullshit. But the good version of it is: - take some valuable task that's never been successfully automated before - do it manually (and expensively) for a while to acquire data - build an automated system with some combination of regular software and ML models trained on the data - now you can do a valuable task for free - scale up and profit The risk is that it's har…

This seems too easy a recipe to be worth it in the medium term - there is no moat. Better cover your data with very strict laws, like google does with its exclusive deals for medical data use.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#8

Post by dfalfndfk »

buboard wrote: post #6
tlb wrote: post #4

There are surely some startups for which this is bullshit. But the good version of it is: - take some valuable task that's never been successfully automated before - do it manually (and expensively) for a while to acquire data - build an automated system with some combination of regular software and ML models trained on the data - now you can do a valuable task for free - scale up and profit The risk is that it's har…

This seems too easy a recipe to be worth it in the medium term - there is no moat. Better cover your data with very strict laws, like google does with its exclusive deals for medical data use.

Like you say it's the access to data itself that is valuable. At risk of oversimplifying: building models is the easy part. Plenty of smart people who can do that.

Data that is expensive to acquire is the best long-term play for an ML company. Either expensive due to regulations or expensive due to the quality of sources. Ideally both.

Re: Ask HN: Why do so many startups claim machine learning is their long game?

#10

Post by kgiddens1 »

The reason this is the case, is that one of the biggest gaps in the market is not the technology itself (most of this is open source or variations there of) but of proprietary data sets. This is valuable when transformed (as my company does at www.edgecase.ai ) to annotated data. What we see is that companies that a) invest in acquiring data and b) transforming said data and c) building a model that is useful to customers (and acquires their data) is the way of the future.

"Ai with unique datasets is an amazing moat"

So in sum:

1) in some cases this is true (but most of the time there is no unique data) 2) if they truly have unique data this is something to take notice of.

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