The Speed of Technological Progress

For an example of how difficult it is to predict the speed of technological progress, let’s take a look at an article in today’s NYT (my emphasis added):

…common diseases, like cancer, are thought to be caused by mutations in several genes, and finding the causes was the principal goal of the $3 billion human genome project. To that end, medical geneticists have invested heavily over the last eight years in an alluring shortcut.

But the shortcut was based on a premise that is turning out to be incorrect. Scientists thought the mutations that caused common diseases would themselves be common. So they first identified the common mutations in the human population in a $100 million project called the HapMap. Then they compared patients’ genomes with those of healthy genomes. The comparisons relied on ingenious devices called SNP chips, which scan just a tiny portion of the genome. (SNP, pronounced “snip,” stands for single nucleotide polymorphism.) These projects, called genome-wide association studies, each cost around $10 million or more

The results of this costly international exercise have been disappointing. About 2,000 sites on the human genome have been statistically linked with various diseases, but in many cases the sites are not inside working genes, suggesting there may be some conceptual flaw in the statistics. And in most diseases the culprit DNA was linked to only a small portion of all the cases of the disease. It seemed that natural selection has weeded out any disease-causing mutation before it becomes common.

So now scientists are adopting a new approach.

In the last few months, researchers have begun to conclude that a new approach is needed, one based on decoding the entire genome of patients.

The new reports, though involving only single-gene diseases, suggest that the whole-genome approach can be developed into a way of exploring the roots of the common multigene diseases.


Dr. Reid said the HapMap and genomewide association studies were not a mistake but “the best we could do at the time.” But they have not yet revolutionized medicine, “which we are on the verge of doing,” he said.

Dr. Goldstein, of Duke University, said the whole-genome sequencing approach that was now possible should allow rapid progress. “I think we are finally headed where we have long wanted to go,” he said.

Sorry for the lengthy excerpt.  If I looked, I could find similar or stronger quotes from other scientists after the human genome was first sequenced, talking about an imminent revolution in medicine.

The question is–now that we know one approach doesn’t work,  have the odds of this new approach working? What are the odds that these scientists are right, and we are on the verge of a medical revolution?

I’m all in favor of technological revolutions. I’m just trying to apply the Black Swan perspective, which suggests that the space of possible scientific investigations is so big that eliminating one approach as failed doesn’t notably raise the possibility of success with a new approach.

I don’t mean this as a critique of science or scientific method.  Rather, I’m assessing this from the top-line economic perspective.  The knowledge that one approach has failed counts as new information. Does this new information raise or lower our assessment of future growth?


  1. I would say it marginally raises. Not only have we learned that one line of research will not work, we developed new tolls in that process. We also learned that we need lots of processing power.


  2. Mike, welcome to research. I used to head a project designed to the world’s largest gene gathering and sequencing respository. The real clue to the above mentioned article is that the experiments performed would cost in total under $1m. The $3b human genome sequence is approaching $1,000 in 2 years. (See Robert Carlson curves) the Moore’s law of genes tech.

    Another important thing to remember is that the human body isn’t a machine or computer, it is an ecology. Your body actually has 10X as many bacteria cells as human cells albeit much smaller. The genetic “code” changes and evolves and the phenotypic expression of the code also changes over time. For example malnutrition or vitamin A deficiency is an environmental factor that may inhibit physical growth or full mental development.

    The revolution in bio tech is just starting in 1-2 years as both genetics and proteomics (genetics functional expression) are better understood in the context of each human (ecology).

    The information flood and discovery like every other “new era” of science will break old hypothesis and make them look silly while ushering in ever new challenges and answers.

  3. This is why thinking you can somehow forecast growth, particularly caused by innovation, has always been a fool’s game. The best you can do is evaluate the low-hanging fruit, eg micropayments will touch off a tech boom soon. Biotech has always been higher up the tree, which is why investors and journalists who fell for their promises of impending revolution were fools. However, that will change someday, as it wasn’t long ago that people were saying the same about computers, with Solow’s famous 1987 quote that “You can see the computer age everywhere but in the productivity statistics.” We clearly see computing in the productivity stats now, it just takes time to develop the technology fully. I don’t doubt biotech will show the same gains someday, but I don’t know enough about it to forecast when that will be.

  4. The Fifth Horseman says:

    let’s take a loo at an article

    A loo?

  5. The Fifth Horseman says:

    For an example of how difficult it is to predict the speed of technological progress,

    It is not hard at all. You just have to look in the right place :

    The Impact of Computing.

  6. The Fifth Horseman says:

    but I don’t know enough about it to forecast when that will be.

    After 2017..

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