Neither the original 1987 story nor the blog post make any sense to me. I feel the authors are focused on a niche aspect: exchanging deep human thoughts with other humans. For that use case, sure, don’t use a computer, don’t use AI.
But there’s nothing wrong with a car made by a robot factory. The car is useful, regardless of how it was made. AI lets some people create new products, those may be useful.
The European Union passed The Artificial Intelligence Act, which classifies:
High-risk – AI applications that are expected to pose significant threats to health, safety, or the fundamental rights of persons. Notably, AI systems used in health, education, recruitment, critical infrastructure management, law enforcement or justice. They are subject to quality, transparency, human oversight and safety obligations
Neither coding nor internal prototypes were the slow part
That is not my experience mentoring 100+ startup founders. Building a prototype, the gateway to serious customer engagement, used to take months and many startups would die before finishing their first one.
Not my experience at all. One slow part was coding. AI takes care of that. But more importantly, the slow part was iterating through concepts, ideas, and prototypes. I thought people on this site embraced lean startups and agile development. AI really helps make that feedback loop 10X faster. I can do an experiment, show it to coworkers and get feedback in a morning, for something that would have taken me almost a week in the past. So now we can try a lot more options, whereas before, we kept getting hit by the sunk cost fallacy: I spent a week on this, I really don't want to start again from scratch with this other approach that may or may not be better.
There is a way to make the campfire approach a little bit less utopian: California could pass a law that makes it legal to have a second temporary job. So that FAANG engineers for instance, would be allowed to campfire/interview at another company with legal protections. Put a bit of grease in the interview system is good for candidates.
the labour cost of having an intern/entry level engineer spend ~30-60s looking through these is likely close to $0.20
Did you do the math? Your estimate feels way off. First, I doubt an intern would process one PR in 30s. Maybe 2-3 minutes, to read 10 lines carefully looking for typos and indentation mistakes. We pay interns close to $100K these days (in a company like CloudFlare), so that's ~80c/minute. My estimate is therefore closer to $1.6 per PR. About 10X.
You are correct that there is a residual value with the intern, over time they would start learning (a little bit) about the code base.
We all have had the client from hell: they don't know what they want, they change their requirements all the time. Whenever they have a new half-baked idea, I need to scramble and re-design the architecture. They have no clue that a small change request has a big impact on the code.
Well... Now I can be that client. And let AI deal with my incomplete, always changing requirements. And get it done anyway.
That matches my experience. At least on the solo front, there were so many topics I wasn't an expert on, that limited what I could build. Now with AI assistance, the sky is the limit. I don't need to be an expert in frontend, backend, I can just build on my personal expertise in a functional domain, and leverage AI to fill in the gaps. I believe many people will benefit from being to build exactly what they want, without gatekeepers or investment.
This paragraph from September 2025 didn't age well:
Like you, we have seen numerous reports that more and more firms are capping their total headcount in favor of leaning on more AI tools, leading to downsizing their intern and new-graduate hiring. [...] But we think this misreads the moment completely, so we’re heading in the opposite direction.
Correct (well, maybe not half a century, maybe 30 years or so). I was just about to reply that I'd love a version of this that shows instructions going in and out of a re-order buffer. That would be enlightening.
You're not wrong, but blocking assignments (and their equivalent in VHDL, variables), are useful as local variables to a process/always block. For instance to factor common sub-expressions and not repeat them. So using only non-blocking assignments everywhere would lead to more ugly code.
Good question. For a long time I think the justification was location: Microsoft is in Seattle, and it’s only the Bay Area that is getting inflated salaries.
Agreed, in my experience, rule 5 should be rule 1. I think I also heard it said (paraphrased) as "show we your code and I'll be forever confused, show me your database schema and everything will become obvious".
Having implemented my shared of highly complex high-performance algorithms in the past, the key was always to figure out how to massage the raw data into structures that allow the algorithm to fly. It requires both a decent knowledge of the various algorithm options you have, as well as being flexible to see that the data could be presented a different way to get to the same result orders of magnitude faster.
In 1993, you could refresh the home page of Netscape (Mosaic) every day and it would mention new sites that had been added. That became unmanageable quickly, which is when two dudes from Stanford started a directory.
We're in early February 2025 [edit:2026] and the article was written on Dec 23, 2025, which makes it less than two months old. I think it's ok not to include a year in the submission title in that case.
I personally understand a year in the submission as a warning that the article may not be up to date.
When I was reviewing such papers, I didn't bother checking that 30+ citations were correctly indexed. I focused on the article itself, and maybe 1 or 2 citations that are important. That's it. For most citations, they are next to an argument that I know is correct, so why would I bother checking. What else do you expect? My job was to figure out if the article ideas are novel and interesting, not if they got all their citations right.
Would those filters be keyword-based only? One benefit of an LLM-based filter I can imagine is that it has a much better understanding of the meaning of text.
Really simple fix: social pressure and expectations should be that every company that uses open source pays a fixed amount of their revenue (is 0.1% low enough to be negligible for the companies). Companies that don't should shunned.
In my dream world, you take that book plus information about yourself (how good of a programmer you already are), feed that into AI and get a customized version that is much better for you. Possibly shorter. Skips boring stuff you know. And slows down for stuff you have never been exposed to. Everyone wins.
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