This is exactly the phrase that crosses my mind when reading the critics of Taleb’s Ten principles for a Black Swan-proof world, mostly from ‘distinguished’ financial ‘experts’. But the dog can bite in this case! I can feel their wrath, often mere desperation and basically, a persistent lack of objective argumentation against Taleb’s postulated theory of inherently unpredictable and highly consequential events. The funny thing is, he pinpointed this kind of intellectual aversion by bankers and ‘experts’ when saying to Timothy Geithner (current Head of United States Secretary of the Treasury) “The center of the problem is that you don’t know what the center of the problem is!”
“Yeah right, like you know” one would say?! Well, when you listen carefully, he only says he doesn’t know… and that’s exactly the point of why these ‘experts’ don’t know what the center of the problem is. Namely by having the illusion they can predict the weather almost perfectly, except for the hurricanes…
Don’t worry, explanation of why this is the case is neatly elaborated on in this article and his book of course, but what the potentially more competent critics who think they understand the financial markets basically want to know is: “Can this guy also walk the walk?”
Well, without understanding all of his mathematical rectifications, by just reading through it my intuition kinda tells me that Taleb does have his statistical calculus together just fine (being a professor of mathematics, risk management and sciences of uncertainty). And eventhough I don’t think one needs the knowledge of fancy statistical theories to figure out how consequential the effects of random events can be, here’s some more technical digest to shut those paradigmatic, self-dellusional and quasi-academic minded tongues up.
So in short, Taleb’s Ten Principles to be Black Swan proof were not taken so seriously by lots of academics who couldnt digest it and criticised these on one ground or another. Now here is Taleb in full technical form in association with Charles Tapiero. They establish why Large Institutions are more vulnerable. Here are some excerpts from the paper available on papers.ssrn.com http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1398102
Too Big to Fail, Hidden Risks, and the Fallacy of Large Institutions
Nassim N. Taleb
Charles S. Tapiero*1
This paper establishes the case for a fallacy of economies of scale in large aggregate institutions. The problem of rogue trading is taken as a case example of hidden risks where rogue traders and losses are considered independently and dependently of the institution’s size. Both independent and dependent loss and hidden positions are shown to lead to the paper’s conclusion, that size and economies of scale have commensurate risks that mitigate the advantages of size.
Naive optimization may lead us to believe in economies of scale that ignores the stochastic structure that results from an aggregation of entities, their associated vulnerabilities and their costs. While companies get larger through mergers and industries become concentrated, based on the premises of “economies of scale” ([Pareto , Taleb ). This does not take into account the effects of an increase of risks resulting from both “dependence” and the latent risks that beset big and small economic entities equally.
For example, the risk of blowups –in fact, under any form of loss or error aversion, and concave execution costs, gains from an increase in size should show a steady improvement in performance, punctuated with large and more losses, with a severe increase in negative skewness ,[ 9].
However, under a nonlinear loss function, increased exposure to rare and latent events may have the effect of raising the costs of aggregation while giving the impression of benefits –since the costs will be borne during rare, but large-impact events. This result is general. It holds not just for economic systems, but for biological, industrial and mechanical ones as well. For example, Fujiwara , using an exhaustive list of Japanese bankruptcy data in 1997 (see also Stanley et al. ,,,,) pointed out to firms failure regardless of their size. Further, since growth of firms has been fed by debt, the risk borne by large firms seems to have increased. As a result, “cemeteries are filled with firms that were too big to fail”.
The growth of size through a growth of indebtedness combined with a “too big to fail” risk attitudes has ushered in as well a moral hazard risk, with firms assuming nonsustainable growth strategies. By the same token, size defined by intensely networked firm (such as large “supply chains” may contribute to supply chain risks (see also Tapiero ,  and Kogan and Tapiero ). Saito  while examining interfirm networks noted that larger firms tend to have more interfirm relationships than smaller ones (and therefore greater dependence) For example, Toyota purchases intermediate products and raw materials from a large number of firms; it has close relationships with numerous commercial and investment banks; it also has a large number of affiliated firms (as this was the case for AIG prior to its failure). Such dependence is particularly acute in some firms where one supplier may control a part needed for the functioning of the whole. For example, a small plant in Normandie, in the North of France with no more than a hundred employees could strike out the whole Renault complex. This networking growth is thus indicative both as a result and as a condition for the growth to sizeable firms of scale free characterisitics (see also ,) but also of the risks sustained. Simulation experiments to that effects were conducted by Alexsiejuk and Holyst  while constructing a simple model of bank bankruptcies using percolation theory on a network of cooperating banks (see also Stauffer on percolation theory ).
Their simulation have shown that sudden withdrawals from a bank can have dramatic effects on the bank stability and may force a bank into bankruptcy in a short time if it does not receive assistance from other banks. More importantly however, a bankruptcy of a simple bank can start a contagious failure of banks concluded by a systemic financial failure.
As a result, too big to fail and its risk moral hazard consequential risk, “too big to bear”, is a presumption that while driving current financial policy and protecting some financial and industrial conglomerates (with other entities facing the test of the market on their own), can be misleading. Size for such large entities thus matters as it provides a safety net and a guarantee by public authorities that whatever their policies, their survivability will be ascertained for the greater good and at the expense of public funding.
The rationality “too big to allow to fail” is therefore misleading, based on a fallacy of aggregates that misrepresent the effects of latent, dependent and rare risks.
Scale is not necessarily robust, in particular with respect to off-model risks. In fact, under loss aversion, the gains from a merger may show a steady improvement in performance, punctuated with large losses, with a severe compensatory increase in skewness. The essential question is therefore can economies of scale savings compensate the risks and fragility they may be subject to.