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inadequatespace

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Absolutely agreed. It feels like tech companies forgot that they are supposed to add value to users.

They literally have forgotten as they’re just following orders from investors (public or private).

Before those orders were “get as many users as possible” as valuation is based on credibility of reaching TAM.

Now the orders are “just use AI as this will be necessary to stay relevant and avoid being displaced” in addition to of course “profitability now”

My sentiments exactly. Incidentally IMO this is arguably more of an anti-authoritative argument, i.e. down on the y-axis, rather than "left" on the x-axis, of the political compass.

Interesting. It's almost like models don't like being ordered around rudely with this "must” language.

Perhaps what they've learned from training data is “must” often occurs in cases with bullshit red tape or other regulations. "You must read the terms and conditions before using this stuff," or something like that, which are actually best ignored.

16. Admitting what you don’t know creates more safety than pretending you do.

Senior engineers who say “I don’t know” aren’t showing weakness - they’re creating permission. When a leader admits uncertainty, it signals that the room is safe for others to do the same. The alternative is a culture where everyone pretends to understand and problems stay hidden until they explode.

It's interesting to contrast this with Sean's statement here www.seangoedecke.com/taking-a-position/

At that point, you need to take a position, whether you feel particularly confident or not.

If you don’t, you’re forcing people with less technical context than you to figure it out themselves

To square the circle, I think the lesson is hide uncertainty to higher-ups, but don't to peers/ other ICs.

Of course, the challenge is that often, unfortunately, both the manager and the other ICs are in the same meeting.

Probably this is one justification of one reason why I hate meetings that include managers.

It’s worth noting that 1. is not mutually exclusive with writing a TODO.

In fact, on my team, all TODOs must have a bug in parentheses immediately afterwards to satisfy the linter. So not only not mutually exclusive, but the opposite.

To be fair, I don't have any great specific ideas, but "Work Without the Worker" for example talks about how a lot of LLMs are fueled by neo-colonialist exploitation.

So I guess broadly speaking there could be strategies involving attempting to influence governmental policy rather than by consumer choice.

Or more radically, trying to change the structure of the government in general such that the above influences actually are more tractable for the common person.

The title of this article implies that it is a major or even the only cause for mass tech layoffs, which I strongly doubt.

For example, rising interest rates I'm sure also independently contributed. I would be interested to if anyone has gotten a sense of exactly how much this has contributed.

I think that’s a bit of a leap; if you think LLMs make the world a worse place, there are many actions that you might take or not take to try to address that.

So, what do all of these responses and the article itself seem to dance around? It's not that it makes developers obsolete, but rather increases inequality. In other words, either creates a class of inferior developers because they don't have whatever new skill, or in the case of offshoring, literally creates a class of lower developers.

Right. What the article is unsurprisingly glossing over (per usual) is that just because AI is perceived (by higher-ups that don’t actually do the work) to speed up coding work doesn't mean it actually does.

and that probably to some extent all involved (depending on how delusional they are) know that it's simply an excuse to do layoffs (replaced by offshoring) by artificially so-called raising the bar to what is unrealistic for most people

PhD training teaches people how to not plateau, as (1) it teaches one how to focus on the unknowns (in that context, in the entire field, so one can dig into them; more easily applicable to one’s own knowledge) and (2) teaches one how to learn without professors / classes, which mostly consists of evaluating the quality of textbooks and/ or research papers in a field, and also how to skim at just the right level so as to learn what one needs to know.

I’ve been doing similar for various frameworks (Flink)

is it right 100% of the time, no.

No human teacher is going to be right 100% of the time either. I suppose ChatGPT is a little more overconfident / doesn’t have a sense of tone or anything to assess confidence; even still, though, given how overconfidence is incentivized in the workplace, it’s not that different in this regard from an average coworker.

Big data is dead 3 years ago

It's only rational. The company certainly doesn't care about that individual first, as evidenced by e.g. its decision to lay them off when it doesn't think the individual is serving them, so why should the individual put the company first?

This is also known as The Iron Law of Institutions.

You must be jocular then. He said serious usage. (In all seriousness, I’ve had few problems as well. Sometimes I have to muck with base images but it’s not a showstopper)