The one thing I can tell you is that pangram is confidently wrong in this instance. And I now worry about how many may have relied on such assessments blindly in consequential places (school essays?). Which ties back to the thesis of my piece - where do you rely on AI and where do you rely on your own intelligence.
On a lighter note, decades ago, in middle school, we had an exercise to summarize a book we read. My school’s librarian wrote ambiguously “write this in your own words”. I asked her what she had meant by that. She had thought I’d copied it from somewhere even though it was all my own words. I went on to become the school topper in my final year for English (and one spot shy of being the school topper for Computer Science).
(We obviously live in a more nuanced world than most social media interactions might make you think :P)
> On a lighter note, decades ago, in middle school, we had an exercise to summarize a book we read.
My first experience with plagiarism was in first grade, when we were told to write a book report about a subject during our library time. I diligently took my book on the musk ox and copied three pages word-for-word into my notebook as my report. I can't remember when or how we learned this wasn't "right", but I still think back on that and laugh.
The valuable engineer is the one who sees the hidden constraint before it causes an outage. The one who notices that the team is solving the wrong problem. The one who reduces a vague debate into crisp tradeoffs. The one who identifies the missing abstraction. The one who can debug reality, not just read code. The one who can create clarity where everyone else sees noise.
"The software engineers who will be most valuable in the future are not the ones who do everything themselves. They are the ones who refuse to spend time on work that A.I. can do for them, while still understanding everything that is done on their behalf."
"The danger is not that A.I. will make people lazy in some vague moral sense. It is that it makes it easy to simulate competence without building competence."
"In that world, the engineer is not replaced by A.I. The engineer becomes more leveraged because they are operating above the level of raw output."
"The ability to explain why something works, not just that it appears to work."
"That process is not optional. It is how engineers acquire and elevate their competency."
"The support system may make you look functional, but it does not make you capable."
"The challenge is not merely adopting A.I. tools. It is protecting the conditions under which real thinking, learning, and craftsmanship continue to thrive."
"They will need interview loops that test reasoning, not just polished answers."
"The organizations that handle this well will not be the ones that simply push A.I. adoption hardest. They will be the ones that learn to separate leverage from dependency, acceleration from imitation, and genuine capability from convincing output."
Also the entire framing around "judgment" and "taste" is what LLMs love to parrot about the topic.
There are fair arguments in the post but I totally agree that "writing is thinking" and also holding myself to "if you didn't bother to write it, why would I bother to read it"?
Think, yes, verifiability, but also tone, terseness etc. and how big those "problem spaces" are in written language versus programming language.