Gerbert & Shira of the Boston Consulting Group, @BCG write in the MIT Sloan Management Review @mitsmr that 'yes, today’s fascination with all things #AI has most of the trappings of a financial #bubble...in most cases there is no clear path for (AI startup) companies to become profitable.' Under the circumstances, small start-ups, having developed their technology, or having run out of venture capital money, will seek to be acquired by larger companies. These acquiring companies, they warn, should be wary. Two things may be worth noting when assessing AI startup companies - first, most algorithms (machine learning especially) used in AI applications are several decades, if not half a century old (a version of backpropagation was, for example, used in the Apollo-11 moon landings). The main new development has been in the development of faster computers on which machine learning algorithms can run, and in the availability of large new data sets on which neural nets/machine learning algorithms can be trained.
But the first development is ironic in a way - in that, neural nets were intended to be archetypes of parallel computer architectures, so the fact that they are simulated in conventional (though very fast) computers of the conventional type should give one some pause. To be sure, there are some new special-purpose hardware architectures (including new chips) which are designed to optimize the computational power used by neural networks. But this has not yet become altogether commonplace. But even when this comes to become more common, rare will be the startup that owns the data which it will use to train its neural network application. The lack of clear ownership of data means that a startup may not be able to optimize and customize its application to the ultimate user, while the machine learning algorithms themselves are publicly available practically, or actually, for free. Thus, on the critical axis of value creation: the trained network, a crucial aspect - the data used - is not owned by the startup. How then can the typical AI startup aim for profitability? What will be its critical determinant of value and distinguishing characteristic? These questions are precisely the ones that acquiring companies will need to ask themselves and the startups they hope to acquire.
While of course one can see the bubble emerging in AI startups, and AI applications, this bubble may not ultimately prove as harmful as a purely financial bubble (eg the 2008 Global Financial Crisis) did. Some good may come even out of AI investments in companies that may ultimately have to be wound up before turning profitable. Some knowledge generation and diffusion will indeed occur, perhaps some patents will be filed or even approved, and ultimately some value may accrue to the investor, though perhaps not at the scale originally envisaged
While of course one can see the bubble emerging in AI startups, and AI applications, this bubble may not ultimately prove as harmful as a purely financial bubble (eg the 2008 Global Financial Crisis) did. Some good may come even out of AI investments in companies that may ultimately have to be wound up before turning profitable. Some knowledge generation and diffusion will indeed occur, perhaps some patents will be filed or even approved, and ultimately some value may accrue to the investor, though perhaps not at the scale originally envisaged
