Yes, people took the Universal approximation theorem[0] as evidence that they only need 1 layer, but there is zero guarantee of efficiency. A single hidden layer may mean many magnitudes more neurons needed over a n-hidden layer, n &gt; 1 network, which could cause an unrealistic training time. Having multiple layers can reduce this training time with an optimal structure.<p>[0]<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Universal_approximation_theorem" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Universal_approximation_theore...</a>