Nice Ad Hominem and Straw Man. you can't actually advocate on any of my points so you resort to attacks and logical fallacies. Name one reason I should continue to engage with someone who is judgemental and hostile and isn't listening like you.
The engineers who hype AI the hardest are the mediocre ones. They lack the skills (or the motivation) to write quality code themselves, so they ask the LLMs to do it for them. Then they get to pretend they wrote it themselves, and a business idiot hands them a gold star.
For cognition, sure, but that's a fairly weak claim. A dog that chases its tail for 3
hours might be considered conscious, but maybe not highly intelligent.
The attention deficit part of ADHD hurts some people, but a high intelligence is able to make up for it in other ways. Attention span is a multiplier for intelligence. Someone with a lower IQ but a longer attention span is able to outperform a higher IQ but shorter attention span, traditionally.
What's required though, is the attention span and the memory to really dig deep into a problem, and then go for a run. If AI makes that easier, since it lets you skip the boring parts and get to the meat of the problem, then hey.
One thing I'll point out is the attention thing might be more of a lack of motivation on my part. It used to be banging out features quickly gave me a nice dopamine rush and the satisfaction of having built something I'm proud of. With LLMs, I don't really have that feeling because even if I guided it to that endpoint I feel less invested and somewhat less interested.
In my experience it's the poorest programmers who thrive with LLMs, because it levels them up. They lacked the skills to design and write quality code before AI, and now they feel like they can compete. They get a computer to write all their code and get to attach their name to it. That's why you see such pushback against AI critics from a vocal subset of engineers; they're the ones who weren't very good.
The engineers who critique AI are the ones who see the garbage code the LLMs write. Just look at the source dump for Claude Code; that code was a rat's nest of epic proportions.
This reads less like an observation about AI and more like someone who thinks very highly of their own judgment and coding ability.
Over the years I’ve worked with a few engineers who talked this way. Ironically, they often ended up being a bigger drag on the team than the “lower skilled” developers they looked down on. Dismissing entire groups of engineers rarely produces much insight.
My experience is that the loudest voices tend to be at the extremes. One side treats LLMs as magic and attributes every productivity gain to AI. The other contributes little beyond “LLMs are garbage and make mistakes.” Neither position is particularly useful.
The reality is probably somewhere in the middle. LLMs are genuinely helpful for many tasks and can make good engineers more productive. They also make mistakes, sometimes serious ones, and still require judgment, design skills, and review. Most engineers I know who use them regularly seem to understand both sides of that tradeoff.
I think the people with extreme positions are often the most useful because they get closer to the source of the argument. Extreme boosters of ai often want to either bypass developing skills to advance their careers or want to exploit what they perceive to be overpaid labor. Extreme pessimists tend to value skill and autonomy and distrust the people with power above them in the hierarchy. They also may identify with their skills and feel existentially threatened by a society that is rapidly devaluing them.
Framing this disagreement as a fundamental misunderstanding of the technical capacity and appropriate use cases, for me, completely misses the plot. Both sides have compelling reasons for their beliefs and the cold rational analysis of the tech is as likely to further entrench the extremes as it is to enlighten.
I will also note that in your comment, you lament the dismissal of entire groups of engineers while doing exactly this when you dismiss the loudest voices (as well as those who think highly of their own ability) and imply that it is the loudest voices who are inherently extreme and therefore inferior to the pragmatic engineer who understands tradeoffs and cost benefit analysis.
I see it slightly differently. It "levels" up the poor programmers in the sense they can submit a ton of output that seems plausible to managers.
But it can also help Sr engineers, differently. They tend to use it in smaller, more tightly scoped use cases. Well scoped re-factoring, boilerplate stuff, improving personal tools, etc. The improvement is not nearly as visible or measurable to managers.
Agree wit you. I like coding and am pretty good about tracking down edge cases to handle, but am so so slow compared to the good programmers. Until now, the company's money (my time) was better spent on other necessary work.
The premise is if they stop training new models then it will become pure profit after 2 years when the hardware finished paying for itself.
It's pretty funny that everyone say that this business is unsustainable, but I have yet seen anyone bankrupt, even the pure hardware providers who are renting out a100 b200.
And AI investors and stock market boosters are just going to accept OpenAI not having anything "new" to show for all their investments? What about replacing hardware once it's been burned out from constant high usage? Is it not odd to you that so many big AI deals get announced and never heard from again? What's the business reason for neoclouds buying GPU's from NVIDIA only for NVIDIA to then pay them to rent them back? How does this make any sense?
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