These are some of the reasons why this model is working nowadays, even though there are advocates who claim incumbents to not really be maximally open (Bostrom, 2016) and to only release technology somehow old to them. My personal view is that companies are getting the best out of spreading their technologies around without paying any costs and any counter effect: they still have unique large datasets, platform, and huge investments capacity that would allow only them to scale up. Regardless the real reasons behind this strategy, the effect of this business model on the AI development is controversial. According to Bostrom (2016), in the short term, a higher openness could increase the diffusion of AI. Software and knowledge are non-rival goods, and this would enable more people to use, build on top on previous applications and technologies at a low marginal cost, and fix bugs.

These are some of the reasons why this model is working nowadays, even though there are advocates who claim incumbents to not really be maximally open (Bostrom, 2016) and to only release technology somehow old to them. My personal view is that companies are getting the best out of spreading their technologies around without paying any costs and any counter effect: they still have unique large datasets, platform, and huge investments capacity that would allow only them to scale up. Regardless the real reasons behind this strategy, the effect of this business model on the AI development is controversial. According to Bostrom (2016), in the short term, a higher openness could increase the diffusion of AI. Software and knowledge are non-rival goods, and this would enable more people to use, build on top on previous applications and technologies at a low marginal cost, and fix bugs.

Investing in AI is not an easy job: AI technologies are black boxes and unless you are able to dig into lines of code they may be inscrutable. Simply looking at proof of concepts might not be enough to really understand the underlying stack behind specific applications, and this represents a big barrier for investors to efficiently allocate their capitals. Generalist investors found then alternative ways to discern investable companies from the pile of tech-driven companies out there. Therefore, if a team is composed of scientists/researchers and has patents (obtained or pending), it would already be a good candidate for an investment even without any revenues. This is driven by top tech companies acquiring smaller startups simply for their'brain power' rather than their actual numbers.

Our smartphone currently represents the most expensive area to be purchased per squared centimeter (even more expensive than the square meters price of houses in Beverly Hills), and it is not hard to envision that having a bot as unique interfaces will make this area worth almost zero. None of these would be possible though without heavily investing in speech recognition research. Deep Reinforcement Learning (DFL) has been the boss in town for the past few years and it has been fed by human feedbacks. However, I personally believe that soon we will move toward a B2B (bot-to-bot) training for a very simple reason: the reward structure. Humans spend time training their bots if they are enough compensated for their effort.