"AI companies simply don’t have the same economic construction as software businesses"
Last I checked, "AI" is software.
Nowhere in here did I read anything about the fundamental truth of modern AI in production:
[AI] is a feature inside a successful product.
There are no "AI" companies, or "AI" products. There are companies which provide services to do inference on data, or in some cases tools/platforms so you can do it on your own.
They also confuse me by using the term services to mean bespoke Non Recurring Engineering efforts, instead of including something like a REST based API service that reflects most "pure" AI companies. Amazon Rekognition or MS Cognitive Services API are perfect examples of SaaS like AI services, but they aren't products exactly because they are used inside some other product as a feature.
At the end of the day, if you look at successful uses of AI in products they are one tiny piece of a larger product, that helps the product scale where it couldn't have before. That's pretty much the only place where it is proving very successful.
Even then, more and more of that is being done in house with the rise of things like Sagemaker and other turnkey ML inference tools.
I agree that this is a common misconception people have about ML and how it fits into our world. If you want to see successful ML products, you don't have to find some AGI stealth mode startup—just look at your phone:
ML functionality is becoming a standard feature in software, and in my opinion, that's the real "ML Revolution." As you mention, turnkey inference tools like Cortex (full disclosure/shameless plug: I'm a contributor) are making this accessible to virtually all eng teams.
The AI companies the article is referencing are trying to provide those examples: smart compose, Netflix recommendation engines, etc. to customer companies without an army of PhD’s. However the article makes it clear that doing that in a general way is hard, much harder than a normal SaaS business.
I don't disagree that providing ML-features-as-a-service in a general way is much harder than a normal SaaS business. However, I don't necessarily believe those sorts of companies are the future of commercial ML. Rather, I believe practices like transfer learning and improved infrastructure (see the turnkey inference platforms mentioned previously) are enabling startups without armies of PhD's to build these ML-powered features themselves.
The services that ML based SaaS are offering are currently harder than the majority of other services.
However you could have said the same about any number of services/products over the years that were in the early adopter curve, which I will state confidently that we're still in for ML.
At the end of the day it's a hard SaaS just like VoIP and search and geoservices once were.
Yeah, the article doesn't say don't do a ML SaaS. Just that it won't have 80% margins like a typical SaaS business and you have to specialize to give you any sort of scale or it's just a Service business.
This is not a tech focused article, it's a business / investor article. It doesn't care about the definition of AI from a tech or software perspective - it cares about classifying types of companies (SaaS vs AI) so that it can make reasonable assumptions about expected performance of those categories and create boundaries / guidelines for how to think about investment strategy.
This is exactly my point, it is a category error in the worst sense as it's creating a category "AI company" that would be charitably described as poorly defined.
My point is that it's poorly defined from a technical perspective but well defined from a business perspective.
If you define AI company from what AI means from a tech perspective, then yes, it's a useless categorization. But if you define AI company as any company that makes money through offering optimization through data as a service, then I think that's a useful definition, in that you can lump many companies together and make sensible generalizations about their business models, which the article does:
Saas business model - 80% margins, exponential scaling, 0 marginal costs, competitive moats through tech
AI business model - 30-50% margins, linear scaling, higher marginal costs, data isn't a great moat
Under the hood it could be deep learning or basic statistics, but it doesn't matter what technology they employ, the method of making money for these types of companies is the same.
(In large/mature markets though, it matters a lot what tech you use since these methods can be copied by your competitors and improved upon - you can't use basic statistics to beat top of the line models forever, so most if not all data companies can be treated as eventual AI companies. I suspect this is why they use the term AI company rather than data company).
> "AI companies simply don’t have the same economic construction as software businesses"
> Last I checked, "AI" is software.
You might be underestimating the importance of data to apply that software onto. I might have some real estate ideas but unless I get some data about properties and actual prices, etc I am just writing a research paper.
Maybe in the US real estate data is more accessible but in other places very much not or it is very expensive. Just an example.
Last I checked, "AI" is software.
Nowhere in here did I read anything about the fundamental truth of modern AI in production:
[AI] is a feature inside a successful product.
There are no "AI" companies, or "AI" products. There are companies which provide services to do inference on data, or in some cases tools/platforms so you can do it on your own.
They also confuse me by using the term services to mean bespoke Non Recurring Engineering efforts, instead of including something like a REST based API service that reflects most "pure" AI companies. Amazon Rekognition or MS Cognitive Services API are perfect examples of SaaS like AI services, but they aren't products exactly because they are used inside some other product as a feature.
At the end of the day, if you look at successful uses of AI in products they are one tiny piece of a larger product, that helps the product scale where it couldn't have before. That's pretty much the only place where it is proving very successful.
Even then, more and more of that is being done in house with the rise of things like Sagemaker and other turnkey ML inference tools.