I fielded a question the other day in Johor, Malaysia about how much harder (or easier) it is to build an AI/ML startup. There are generally building blocks for any early stage startup, and they are:
invest initial capital
Now I'll comment on the main differences in each category:
team: more technical or academic talent can help, but not necessary with open and available frameworks and MOOCs such as [this](http://www.kdnuggets.com/2016/09/machine-learning-year-total-noob-effective-practitioner.html#.V-AVvk__XeY.facebook) and [Andrew Ng's course](https://www.coursera.org/learn/machine-learning)
MVP: the process of building an MVP is similar if not same. Timing could be just as similar.
invest initial capital: I initially thought that this could be slightly more, based on initial salaries; but at early stage there are no salaries to contend with. However, there are at least living costs.
gain users: same tactics and should be similar marketing costs as traditional startups. Some thoughts on tactics [here](http://simplystatistics.org/2016/03/30/humans-as-training-set/).
mentoring: more technical and AI specific needed
follow-on capital: definitely slightly more, based on salaries for hiring