{
  "$type": "site.standard.document",
  "canonicalUrl": "https://rednafi.com/zephyr/diminishing-half-life-of-knowledge/",
  "description": "How technical skills decay faster than ever in software engineering, and strategies for continuous learning in a rapidly changing field.",
  "path": "/zephyr/diminishing-half-life-of-knowledge/",
  "publishedAt": "2023-11-12T00:00:00.000Z",
  "site": "at://did:plc:fgtm2c26vfcj74rfmeggbyqj/site.standard.publication/3mnl6f7ob462z",
  "tags": [
    "Essay"
  ],
  "textContent": "Ever been in a situation where you landed a software engineering job with a particular tech\nstack, mastered it, switched to another company with a different stack, nailed that too, and\nthen found yourself in a third company that used the original stack? Now, you suddenly sense\nthat your hard-earned acumen in that initial stack has not only atrophied over the years but\nalso a portion, or all of it, has become irrelevant, making it a bit of a struggle to catch\nup with the latest changes.\n\nAfter graduation, I switched gears from electrical to software engineering. I started out as\na junior data scientist at an e-commerce startup. There, I juggled tasks like training\nsmall-scale machine learning models, analyzing tabular data, and building visualizations\nwith the Python data stack. When COVID hit, I jumped ship to another company using a\ndifferent tech stack, shifting my attention to distributed system and backend engineering.\n\nIn my current role, I'm still mostly doing backend work, just with a different set of tools\nthan before. Throughout this journey, I've been fortunate enough to dart around three\ndifferent continents. While hopping between positions and tech stacks has definitely widened\nmy horizon, I've been grappling with the observation that as I pick up new skills, some of\nthe older ones depreciate at a faster pace. It's quite difficult to keep yourself sharp with\ntools that you don't get to use regularly at work.\n\nWith all the hype around LLMs lately, I'm feeling drawn back to my original data science\nroots. To rekindle that part of my brain, I've started dabbling in some of my old OSS work,\nand to my great surprise, I'm struggling quite a bit to pick up the fundamentals and the\nrequired mathematics since I've been out of the game for so long. While I've kept in touch\nwith some part of the open-source data world to stay relevant, apparently that wasn't\nenough. On top of that, the world keeps piling on new concepts and skills that I'll need to\npick up if I ever intend to get past the interview barrier and professionally work in this\narena again.\n\nTurns out this manifestation of stochastic knowledge decay is a well-studied phenomenon. The\nterm [half-life of knowledge] was coined in 1962 by economist [Fritz Machlup] to represent\nthe amount of time that has to elapse before half of the knowledge or facts in a particular\narea is superseded or shown to be untrue. It's named after the half-life of decay in\nradioactive materials. This [IEEE Spectrum article] dives deep into the concept and reflects\nupon its effect on the industry. It postulates that the half-life of engineering knowledge\nis shrinking faster than ever before and the only way to tackle this is through continuous\nlearning and getting better at managing the onslaught of information.\n\nI don't have a prescriptive solution for this. I wrote this text to start a discussion\naround a feeling I previously struggled with but didn't know how to label. So far, engaging\nwith the OSS community on topics I find exciting, taking meticulous notes, tracking my\nlearning progress, adopting boring technology, and writing about them have helped me stay\nrelevant. However, this approach isn't bulletproof and is quite susceptible to lack of\nmotivation at the tail end of a 40-hour workweek. If you've experienced something similar\nand found a solution that worked for you to some extent, I'd love to hear about it!\n\n\n\n\n[half-life of knowledge]:\n    https://en.wikipedia.org/wiki/Half-life_of_knowledge\n\n[fritz machlup]:\n    https://en.wikipedia.org/wiki/Fritz_Machlup\n\n[ieee spectrum article]:\n    https://spectrum.ieee.org/an-engineering-career-only-a-young-persons-game",
  "title": "The diminishing half-life of knowledge"
}