Every time a disruptive technology becomes available, humanity goes through essentially the same cycle: At first, exaggerated fear and sulking rejection on the one side clash with overflowing enthusiasm on the other. As the technology matures and its capabilities and limitations become more distinct, eventually a synthesis—often grounded in rules and regulations about where and how it can be most beneficially applied—emerges. Thus, what once seemed new, outlandish, and groundbreaking turns into a commodity, and often one that helps to shape the next level of innovation.

As a kid, I’ve enjoyed stories in which inanimate objects suddenly came to life, either through magic or with the help of science. Mary Shelley’s Frankenstein, for example. Or that old Jewish myth of the Golem: A figure, formed of clay, is brought to life by a Rabbi in order to liberate the holy man of his most tedious chores. No more cooking, cleaning, chopping wood, or carrying water.

Marc Andreessen’s recent article “Why AI Will Save the World” is—sadly—an example of how polarized society and public discourse have become, particularly on such high-stakes topics as the opportunities and risks posed by the proliferation of AI. While I largely applaud (and mostly agree with) his optimism about the tremendous potential of AI to improve virtually every aspect of human life and flourishing, some of his arguments against AI regulation are not only absurd, but threaten to unnecessarily poison this important debate.

Tracking the search engine market feels almost mesmerizing these days—a bit like staring into a lava lamp and wondering what’s going on in there. After Google having dominated the field practically forever, with Microsoft’s Bing struggling to even register as a contestant, competition apparently surged and almost immediately plummeted again. At first, it seemed as if Google had overlooked the potential disruption of the traditional, stateless search interface by conversational and interactive experiences that ChatGPT and others offer.

Language is arguably humanity’s most consequential invention. Unlike any other species, Homo Sapiens has developed a means by which complicated, abstract plans can be efficiently and effectively transmitted from one mind to the next. Thus, our prehistoric ancestors, in contrast to their natural rivals, could successfully coordinate, say, a large-scale hunt, an attack on a foreign tribe, or even how they wanted to arrange life in increasingly complex social communities.

The advent of ChatGPT and the subsequent release of GPT-4 have brought a wave of publicity to the field of generative AI. Many observers marvel at the levels of “creativity” that the technology has attained seemingly overnight and it’s tempting to get lost in pondering its implications on the future of, say, education, white-collar work, healthcare, or any other sector of the economy that suddenly looks ripe for disruption.

If you’re reading this, the mounting news of apparent breakthroughs in generative AI have surely not passed you by: Tools that can compose music, write code, paint pictures, and genuinely seem to be smart enough to pass various state exams have become publicly (and mostly freely) available in a surprisingly short timespan. This wave of commoditization has also shed the light of public discourse onto debates about AI safety which had, until now, been confined to relatively small, mostly academic circles.

Designing Your Work Life by Bill Burnett and Dave Evans. “F**k this s**t, I’m quitting!” Many of us have played with the thought of making a memorable exit from a job we didn’t enjoy at some point in our careers. I know a few people who actually did (though not with quite such strong language), but unsurprisingly that didn’t end well for any of them.

In many instances software architecture is less a product of deliberate design and more an accidental result of countless interactions between people. Or, as Eric Raymond put it more pointedly: “If you have four groups working on a compiler, you’ll get a 4-pass compiler.” How Do Committees Invent? by Mel Conway. This idea was originally introduced by Mel Conway in his 1967 paper “How Do Committees Invent?

The other day I had a wonderful conversation with a friend and fellow software enthusiast about fairness when it comes to the remuneration of knowledge workers. Both of us have been through various posts in different technology companies, increasingly concerning ourselves with management and leadership as our careers progressed. Our conversation honed in on the following question: Is the way people are payed in our industry today fair? Note that I’m not referring only to software developers here—data scientists, UI experts, DevOps heroes, product managers, and all the others who contribute to successful projects are included as well.