Blog Post | The Reality of “Scaling AI Startups” | By Matt Turck

Source: @mattturck | February 25, 2019 Author: Matt Turck Not so long ago, AI startups were the new shiny object that everyone was getting excited about.

  It was a time of seemingly infinite promise: AI was going to not just redefine everything in business, but also offer entrepreneurs opportunities to build category-defining companies.

A few years (and billions of dollars of venture capital) later, AI startups have re-entered reality.

 Time has come to make good on the original promise, and prove that AI-first startups can become formidable companies,  with long term differentiation and defensibility.

In other words, it is time to go from “starting” mode to “scaling” mode.

To be clear: I am as bullish on the AI space as ever.

  I believe AI is a different and powerful enough technology that entire new industry leaders can be built by leveraging it, as long it is applied to the right business problems.

At the same time, I have learned plenty of lessons in the last three or four years by being on the board of AI startups, and talking to many AI entrepreneurs in the context of Data Driven NYC.

   I’ll be sharing some notes here.

This post is a sequel to a presentation I made almost three years ago at the O’Reilly Artificial Intelligence conference, entitled “Building an AI Startup: Realities & Tactics“, which covered a lot of core ideas about starting an AI company:  building a team, acquiring data, finding the right market positioning.

A lot of those concepts still hold, and this post will focus more on specific lessons around scaling.

Some definitions To get the semantics out of the way: as a result of market hype over the last few years, it has become unclear what an “AI startup” actually is.

There are basically three categories of AI startups: “AI-first” startups are startups whose product simply could not function without AI at its core, whether they serve consumers or enterprises.

 AI is front, left and center.

Startups providing AI tools and infrastructure (software and hardware), both to other startups and Fortune 1000 customers; and Startups that use AI as part of as part of a broader product or technology stack.

 This includes a lot of companies that were started before the current wave of excitement around AI, and have added AI capabilities to their pre-existing software product.

This post is mostly about “AI-first” startups, although some lessons may apply to other categories, and perhaps to “deep tech” (or “frontier tech”) startups in general.

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