alt Hacker NewsThe Future of Everything Is Lies, I Guess: New Jobs
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I think the reason AI isn't going to replace CEOs, or anyone in the C suite, is pretty obvious. They see themselves as the company. Everyone else is a resource. AI is here to replace resources, just like investing in a brand new lawn mower. For them, replacing an executive with AI is like saying you're going to marry a broom.
The problem with AI is that it isn't like any previous technology. There may be temporary jobs to fill in the gaps but they won't be careers. The AI will do the process engineering and self optimization. The prompt witchcraft is a good example because today its totally unnecessary and doesn't actually increase performance, and they'll continue to make it easier to direct/steer the models.
We're literally trying to build an intelligence to replace us.
I think that this is an interesting attempt at taxonomy, but it's a bit on the magical thinking end (and I say this as somebody that does a good amount of what's described as the incanter role). It's a combination of the author's previous witchy aesthetic (see his excellent "<x>ing the technical interview" series) and progressive labor politics (which are asymptotically doomed in the current automation push).
The biggest failure of imagination, I think, is the assumption we'd use humans for most (or *any) of these jobs--for example, the work of the haruspex is better left to an LLM that can process the myriad of internal states (this is the mechanical interpretation field).
Loved that section about "meat shields". LLMs cannot be held accountable. Someone needs to be involved in decision making, with real stakes if those decisions are bad.
I don't understand the title. It doesn't seem exactly clickbait but also doesn't seem to be what the article is about?
Anyway: The new job types might seem overspecialized now but history shows us this is indeed what happens as new industries open up. I think these predictions look quite solid.
With AI, you just have to choose between going slow vs making a huge blunder later at some point.
If you go fast, you are bound to come across AI bugs later. Then you ultimately slow down to fix them. Which takes more time.
That black box will keep evolving. The AI interpreter will have to keep catching up with it.
All plausible, but not very transformative. Like imagining that the new jobs enabled for the automobile include automobile maintenance, tire shops, and so on. Traveling nurses, motel operators, military tanks, doordash, suburban life, beer sales at NASCAR, those were all enabled by the car (and its larger sibling the truck). Still missing are the jobs snd industries enabled by "AI" that are not themselves "AI".
As an engineer, I'm never more excited about this job.
My implementation speed and bug fixing my typed code to be the bottleneck - now I just think about an implementation and it then exist - As long as I thought about the structure/input/output/testability and logic flow correctly and made sure I included all that information, it just works, nicely, with tests.
Unix philosophy works well with LLM too - you can have software that does one thing well and only one thing well, that fit in their context and do not lead to haphazard behavior.
Now my day essentially revolves around delivering/improving on delivering concentrated engineering thinking, which in my opinion is the pure part about engineer profession itself. I like it quite a lot.
"Unavailable Due to the UK Online Safety Act" - without my VPN... do you know why?
we are in the times of irrational exuberance - rationality will set in soon!
Humans will be held accountable, not machines, whatever is the technology used. The jobs you suggest are based on the state of LLM right now, this could change rapidly, considering the state of progress. These are just activities that are already done by people that work with these instruments, because they want to optimize and obtain the best/safest output from these machines.
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This is part 9 of a 10-part series. The author has posted every chapter to Hacker News every day for the past 9 days. Every time four of the first five or so comments are:
Someone noting it is unavailable in the UK.
Someone posting an archive.is link.
Someone asking why the above posted an archive link to a static site.
An answer that it is because the content is otherwise unavailable in the UK.
Do we really need to see this every single time?
I realize I am also not adding to the real discussion now as well, but Jesus Christ, this is irritating. Can we get a new rule that an author posting their own content, knowing it is unavailable in the UK, has to post their own archive link and explain why they're doing so as part of the submission?
Is there a way back to calling human beings human beings and not "meat"? Or is the sociopathic Jeffrey Dahmer undertone now the new normal?
I am personally of the opinion that ML will end up being 'normal technology', albeit incredibly transformative.
I think you can combine 'Incanters' and 'Process Engineers' into one - 'Users'. Jobs that encompass a role that requires accountability will be directing, providing context, and verifying the output of agents, almost like how millions of workers know basic computer skills and Microsoft Office.
In my opinion, how at-risk a job is in the LLM era comes down to:
1: How easy is it to construct RL loops to hillclimb on performance?
2: How easy is it to construct a LLM harness to perform the tasks?
3: How much of the job is a structured set of tasks vs. taking accountability? What's the consequence of a mistake? How much of it comes down to human relationships?
Hence why I've been quite bullish on software engineering (but not coding). You can easy set up 1) and 2) on contrived or sandboxed coding tasks but then 3) expands and dominates the rest of the role.
On Model Trainers -- I'm not so convinced that RLHF puts the professional experts out of work, for a few reasons. Firstly, nearly all human data companies produce data that is somewhat contrived, by definition of having people grade outputs on a contracting platform; plus there's a seemingly unlimited bound on how much data we can harvest in the world. Secondly, as I mentioned before, the bottleneck is both accountability and the ability for the model to find fresh context without error.