775
Generative AI Is an Engineering Disaster. A shockingly inefficient trillion-dollar project.
(www.theatlantic.com)
this post was submitted on 15 Jul 2026
775 points (97.5% liked)
Technology
87550 readers
3482 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related news or articles.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 3 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
Having worked closely with junior engineers and with high-end models, I strongly disagree. In almost all cases the output of the juniors is on par or better (albeit slower) than the LLMs ,and unlike an LLM, a junior actually learns and becomes much more consistent than the AI over time.
Are you talking about a one shot from the model or using a harness? I agree a junior dev can do better then a one shot, but with a proper harness with adversarial review cycles I don't think a junior dev could
A proper harness will have memory and will get more consistent the more you use it. You can "teach" it by adding skills and having it write it's learnings either locally or to repo context files.
Yeah, by creating a bunch of .md files of questionable quality that get fed into an already-limited context window, on top of a pile of all sorts of other context cruft from the harness, the model provider, and whatever else is propping up the system to give the illusion of intelligence...
I've experimented with harnesses quite a bit and I don't understand the hype. The difference in quality has been middling in my experience, but the token burn has been significantly more — which makes sense when you understand that the more of the context window you use, the worse most models perform.