I worked at Element AI, and that was true from the inside to an extent that was borderline terrifying. On orientation we were told that EAI's strategy was to hire as many smart individuals as possible. There was no focus on delivering an actual product, it was demo after demo of semi-impressive DL models.
It makes me sad thinking of all the brilliant colleagues I had that were simply wasting their talent.
What do you believe the go-to-market strategy is of those two?
For C3.ai, my interpretation was they're trying to become the Microsoft of ML, re-packaging the entire ML pipeline into a more consumable developer experience. They seem currently focused more upon the model analytics part of the pipeline than say, training data selection or ETL to ingest raw data (whether to train upon or run in production), or any number of other pieces of the pipeline.
Landing.ai appears to be more tightly focusing their messaging on the operational aspects of the ML pipeline, though not so much the modeling part that C3 appears to emphasize. They also seem to very tightly narrow their focus on machine vision ML.
I'm probably wildly off though, having had no access to the actual platforms and ever used them in anger.
I attended a presentation by Tom Siebel, founder of C3, around 2011 who said the "inspiration" from C3 came from hiring ~10 of the best management consultants from McKinsey who told him that Enterprise IoT was going to be a big deal. My guess is the AI part came later as a way of productizing a data processing pipeline.
The event was strange and really disabused me of many assumptions I had about startup origin stories.
Enterprise IoT is going to be a big thing... You know because as Deloitte says to paraphrase, "your stuck on a plane that's going nowhere because of a technical.. turns out its a 10cent part.. how are you going to get there? You need a smart factory ecosytem, ... it's not a supply chain but a supply network" or however, the podcast ad goes.
C3, Palantir, Salesforce and probably a few other companies / consults are investing in this space. It's the whole integrate all the data silos into some cloud, public or private, with +snowflake style data storage and build your business around data in order to drive real time feedback to solve customer problems.
But you are not wrong, "productizing a data processing pipeline" is big business.
It makes me sad thinking of all the brilliant colleagues I had that were simply wasting their talent.