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The unreasonable effectiveness of data is possibly the greatest surprise coming out of the last twenty years of Artificial Intelligence (AI): pretty simple algorithms and tons of data seem to almost invariably beat complex solutions with small-to - none training set. In the seminal words of (Halevy, Norvig, and Pereira, 2009): "now go out and gather some data, and see what it can do". The perfect storm has been set in motion by the convergence of the big data hype (Hagstroem et al 2017), the general availability of specialized hardware and scalable infrastructure, and some "computational tricks" (e.g. Hochreiter S., Schmidhuber S., 1997, Hinton et al, 2013): all together, they unlocked the Deep Learning (DL) Revolution and created a tremendous amount of business value (Chui et al 2018). The A.I. wave is so disruptive that a great deal of commentators, practitioners (Radford et al 2019) and entrepreneurs (Musk 2017) inevitably started to wonder what is the place of humans in this new world: is A.I. going to replace humanity (in the world of Silicon Valley, Joy in 2001 was already stating that "the future doesn't need us")? In this position paper, we shall argue for two surprising perspectives: 1) the future of A.I. is about less data, not more; 2) human-machine collaboration is, at least for the foreseeable future, the only way to outpace humans and outsmart machines effectively. The paper is organized as follows: Section 2 contains a review of the current state of the A.I. landscape, with particular attention to the origins of the DL Revolution; the section casts some doubts on the general applicability of DL to language problems, drawing from theoretical considerations from academia and industry use cases in the space of Tooso. Section 3 details a real use-case from the industry that is challenging for the DL paradigm, and outlines a different framework to tackle the problem; finally, Section 4 concludes with remarks and roadmap for a new type of A.I., what we call "A.I. with humans and for humans." 2