Month: November 2024

When it gets too much or Revenge of the Tipping point…

BIld av Katja S. Verhoeven från Pixabay

https://www.bokus.com/bok/9780316575805/revenge-of-the-tipping-point-overstories-superspreaders-and-the-rise-of-social-engineering/?utm_campaign=Performance%20Max%20%7C%20English%20%7C%20Rooth&gad_source=1&gclid=Cj0KCQiAuou6BhDhARIsAIfgrn4spmK1A21PF2Luov0HXzMwMFMsTcJKUSsvnIH5UEfxDs_lBz3TOUMaAuLEEALw_wcB

I’ve just finished reading this great book about the way in which the tipping point tips to the wrong side. It’s mostly about the law of “The large effect of the few” as Malcolm Gladwell puts it. In short, this law means that in certain situations, it’s the minority that is responsible for large effects. For example, the minority of old, badly maintained cars that contribute to to over 55% of pollution in one of the US cities. It’s about when one person, a superspreader, ends up in very specific conditions that allow this person to spread the contagion of the COVID virus at the beginning of the pandemic.

Now, we see that in software engineering a lot when we look at the tooling that we use. Let’s take the CI/CD tool Jenkins as an example. It is one of many different tools that were on the market at that time. It was not even the major one, but it was a sibling to a professional tool that was maintained by Oracle (if I recall correctly). Yet, it became very popular and the other tools did not. Since they were siblings, they were not worse, not better either; maybe a little different. What made it tip was the adoption of this tool in the community. A few superspreaders started to use it and discovered how good the tool is for automation of CI/CD tasks.

I see the same parallel to AI today. What was it that tipped the use of AI? IMHO it was a few things:

  1. Google’s LSTM use in Search – since there was a commercial value, it made sense to adopt it. Commercial adoption means business value, improvement and management focus (funding).
  2. Big data – after almost a decade of talking about big data, collecting it and indexing it, we were ready to provide the data-hungry modules with the data they needed to do something useful.
  3. HuggingFace – our ability to share models and use them without requirements on costly GPUs and large (and good) datasets.
  4. Access to competence – since we have so many skilled computer scientists and software engineers, it was easy to get hold of the competence needed to turn ideas into products. Google’s Deepmind is a perfect example of it. People behind it got the Nobel Prize.

Well, the rest is history as they say…. But, what will the next invention on the verge of the tipping point be?