All of this might be true currently, but that's because this current first generation "AI" (technically should just be called ML) is mostly bullshit. To clarify, I don't mean anyone is lying or selling snake oil - what I mean by bullshit is that the vast majority of these services are cooked up by software developers without any background in mathematics, selling adtechy services in domains like product recommendation and sentiment analysis. They are single discipline applications accessable to devs without science backgrounds and do not rely on substantial expertise from other fields. That makes them narrow in technical scope and easy to rip off (hence no moat, lots of competition, and human reliance and lack of actual software).

The next generation of Machine Learning is just emerging, and looks nothing like this. Funds are being raised, patents are being filed, and everything is in early stage development, so you probably haven't heard much yet - but these ML startups are going after real problems in industry: cross disciplinary applications leveraging the power of heuristic learning to make cross disciplinary designs and decisions currently still limited to the human domain.

I'm talking about the kind of heuristics which currently exist only as human intuition expressed most compactly as concept graphs and, especially, mathematical relationships - e.g. component design with stress and materials constraints, geologic model building, treatment recommendation from a corpus of patient data, etc. ML solutions for problems like these cannot be developed without an intimate understanding of the problem domain. This is a generalist's game. I predict that the most successful ML engineers of the next decade will be those with hard STEM backgrounds, MS and PhD level, who have transitioned to ML. [Un]Fortunately for us, the current buzzwordy types of ML services give the rest of us a bad name, but looking at these upcoming applications the answers to the article tl;dr look different:

>Deep learning costs a lot in compute, for marginal payoffs

The payoffs here are far greater. Designs are in the pipeline which augment industry roles - accelerate design by replacing finite methods with vastly quicker ML for unprecedented iteration. Produce meaningful suggestions during the development of 3D designs. Fetch related technical documents in real time by scanning the progressive design as the engineer works, parsing and probabilistically suggesting alternative paths to research progression. Think Bonzi Buddy on steroids...this is a place for recurring software licenses, not SaaS.

>Machine learning startups generally have no moat or meaningful special sauce

For solving specific, technical problems, neural network design requires a certain degree of intuition with respect to the flow of information through the network, which both optimizes and limits the kind of patterns that a given net can learn. Thus designing NN for hard-industry applications is predicated upon an intimate understanding of domain knowledge, and these highly specialized neural nets become patentable secret sauces. That's half of the most - the other comes from competition for the software developers with first-hand experience in these fields, or a general enough math heavy background to capture the relationships that are being distilled into nets.

>Machine learning startups are mostly services businesses, not software businesses

Again only true because most current applications are NLP adtechy bullshit. Imagine coding in an IDE powered by an AI (multiple interacting neural nets) which guides the structure of your code at a high level and flags bugs as you write. This, at a more practical level, is the type of software that will eventually change every technical discipline, and you can sell licenses!

>Machine learning will be most productive inside large organizations that have data and process inefficiencies

This next generation goes far past simply optimizing production lines or counting missed pennies or extracting a couple extra percent of value from analytics data. This style of applied ML operates at a deeper level of design which will change everything.