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And the reason for Purdue [offering a masters program on semiconductors in 1960] is most people don’t know that the transistor effect was found at Crane Naval Air Development Center three weeks after Shockley. And it’s the tragedy of being second.

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Most people who succeed are not very objective or realistic about why. They think it's all their personal brilliance. And in significant part it is, but they forget the luck factor and the contribution of other people and timing and the economy and all the rest of it. People who fail often times are desperately in need of a success. They're smarter, they're more clever about how they do things, their sometimes tremendous egos are suspended in check.


So we rarely will finance somebody at Sequoia who's had an outrageous success. My - my best example is my friend Steve Jobs. We financed Steve in 1977 at Apple. Steve was twenty, un-degreed, some people said unwashed, and he looked like Ho Chi Min. But he was a bright person then, and is a brighter man now. And here was a man that created Apple, and in the creation of Apple helped create the personal computer business. Phenomenal achievement done by somebody in his very early twenties. Outrageously success - successful, and after he - his stay at Apple he then evolved to an individual who was having lunch with the governor of California, then Jerry Brown, who had an apartment in New York City. When I met him he didn't know where New York City was.

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Another interesting quote from McLane&Moerdijk, 1999.

In short, we can create two contrasting categories: a poset P, which has one morphism and many objects, and a group G, which has one object and many morphisms.

Now, by thinking in topos terms, we can see that both categories have a terminal object ( "the arrow", "the object") and can be used in combination with a subobject classifier. The existence of a subobject classifier creates an opportunity for a simple decision making process. Then the question arises, "Decision making about what?"

One way to answer it would be to go back to Adam Smith and say, "Specialization!" That is, posets P enable us to specialize in arrows ("the arrow"), while groups G (including monoids) let us specialize in objects ("the object".) A combination (e.g. a disjoint union) of these two categories helps us build a broad range of large, highly specialized categories with relatively simple decision making rules. I bet most industrial systems can be modeled this way.
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Marshall McLuhan, The Gutenberg Galaxy, p 219
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...in fact modernity is a surprisingly simple deal. The entire contract can be summarised in a single phrase: humans agree to give up meaning in exchange for power.

--- Yuval Noah Harari. “Homo Deus: A Brief History of Tomorrow.”
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The notion that an economically mature technology can be modeled as a free monoid is trivial because the technology is going to be completely described and communicated by standardized instructions (mechanical, logical, verbal, etc.), wherein the instructions are concatenated sentences from a specialized dictionary.

The good news is that when we have an inkling of a promising idea we can imagine how it is going to look like when fully matured. The bad news is that there's no dictionary yet to imagine it from.
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75 percent of Chinese respondents are inclined to ride in an autonomous taxi, compared to 52 percent of Americans.

Within a decade, Wu Hu, about 200 miles west of Shanghai, aims to become the first city in the world to ban human drivers and go fully autonomous.

A large-scale experiment of that sort is far more likely in China than the US or EU because the country “doesn’t necessarily have to have the debate to reach a conclusion,” says Burns.

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Over the last 60 years, Asia absorbed an enormous amount of technology transfer from the US. Moreover, they are now capable of technology development themselves. By contrast, Germany does very little technology transfer to the rest of the world. Their education system is geared toward internal absorption, rather than export.
When it gets down to it — talking trade balances here — once we've brain-drained all our technology into other countries, once things have evened out, they're making cars in Bolivia and microwave ovens in Tadzhikistan and selling them here — once our edge in natural resources has been made irrelevant by giant Hong Kong ships and dirigibles that can ship North Dakota all the way to New Zealand for a nickel — once the Invisible Hand has taken away all those historical inequities and smeared them out into a broad global layer of what a Pakistani brickmaker would consider to be prosperity — y'know what? There's only four things we do better than anyone else:
microcode (software)
high-speed pizza delivery

― Neal Stephenson, Snow Crash. 1992.
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I advance the hypothesis here that technical change in general can be ascribed to experience, that it is the very activity of production which gives rise to problems for which favorable responses are selected over time.
I therefore take instead cumulative gross investment (cumulative production of capital goods) as an index of experience. Each new machine produced and put into use is capable of changing the environment in which production takes place, so that learning is taking p!ace with continually new stimuli. This at least makes plausible the possibility of continued learning in the sense, here, of a steady rate of growth in productivity.

The Economic Implications of Learning by Doing
Author(s): Kenneth J. Arrow
Source: The Review of Economic Studies, Vol. 29, No. 3 (Jun., 1962), pp. 155-173

The distinguishing feature of the technology as an input is that it is neither a conventional good nor a public good; it is a non-rival, partially excludable good.

The main conclusions are that the stock of human capital determines the rate of growth, that too little human capital is devoted to research in equilibrium, that integration into world markets will increase growth rates, and that having a large population is not sufficient to generate growth.
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"technology is the sum total of what we know about how to use labor and capital ouput. also often refered to as productivity.
25-30% of improvement in productivity comes from new [business] machinery. the rest comes from better business organization and management ( US data).

y=f(N,K,A)=A*Kx*N1-x ( Duglas production function).

nb: technology influence on income per worker. Y/N



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