The article title is click bait here is the full article:
Wondering what your career looks like in our increasingly uncertain, AI-powered future? According to Palantir CEO Alex Karp, it’s going to involve less of the comfortable office work to which most people aspire, a more old fashioned grunt work with your hands.
Speaking at the World Economic Forum yesterday, Karp insisted that the future of work is vocational — not just for those already in manufacturing and the skilled trades, but for the majority of humanity.
In the age of AI, Karp told attendees at a forum, a strong formal education in any of the humanities will soon spell certain doom.
“You went to an elite school, and you studied philosophy; hopefully you have some other skill,” he warned, adding that AI “will destroy humanities jobs.”
Karp, who himself holds humanities degrees from the elite liberal arts institutions of Haverford College and Stanford Law, will presumably be alright. With a net worth of $15.5 billion — well within the top 0.1 percent of global wealth owners — the Palantir CEO has enough money and power to live like a feudal lord (and that’s before AI even takes over.)
The rest of us, he indicates, will be stuck on the assembly line, building whatever the tech companies require.
“If you’re a vocational technician, or like, we’re building batteries for a battery company… now you’re very valuable, if not irreplaceable,” Karp insisted. “I mean, y’know, not to divert to my usual political screeds, but there will be more than enough jobs for the citizens of your nation, especially those with vocational training.”
Now, there’s nothing wrong with vocational work or manufacturing. The global economy runs on these jobs. But in a theoretical world so fundamentally transformed by AI that intellectual labor essentially ceases to exist, it’s telling that tech billionaires like Karp see the rest of humanity as their worker bees.
It seems that the AI revolution never seems to threaten those who stand to profit the most from it — just the 99.9 percent of us building their batteries.
ML already has demonstrated tremendous capability increases for automated machines, starting with postal letter sorters decades ago, proceeding through ever more advanced (and still limited, occasionally flawed - like people) image recognition.
LLM puts more of a "natural language interface" on things, making phone trees into something less infuriating to use and ultimately more helpful.
That's a matter of application
Yeah, although I can see LLMs being helpful as a front end, in addition to the traditional checklist systems used for safety regulation, medical Dx and other guidance, an LLM can (and has, for me) provided (incomplete, sometimes flawed) targeted insights into material it reviews - improving the human review process as an adjunct tool, not as a replacement for the human reviewer.
Definitely. Mostly I have been using LLM generated code to create deterministic processes which can be verified as correct - it's pretty good at that, I could write the same code myself but the AI agent/LLM can write that kind of (simple) program 5x-10x faster for 10% of the "brain fatigue" and I can focus on the real problems we're trying to solve. Having those deterministic tools again makes review and evaluation of large spreadsheets a more thorough and less labor intense process. People make mistakes, too, and when you give them (for this morning's example) a spreadsheet with 2000 rows and 30 columns to "evaluate" - beyond people's "context window capacity" as well... we need tools that focus on the important 50 lines and 8 columns without missing the occasional rare important datapoints...
The better modern models, in roughly the past 10 months or so, have turned a corner for some computer programming tasks, and over those 10 months they have improved rather significantly. It's not the panacea revolution that a lot of breathless journalists describe, but it's a better tool assisting in the creation of simple programs (and simple components of larger programs) than anything I have used in the previous 45 years, and over the past 10 months the level of complexity / size of programs the LLMs can effectively handle has roughly tripled, in my estimation for my applications.
When it's used for worthless garbage (as most of it seems to be today), I agree with this evaluation. Focused on good use cases? In specifically good use cases, the power / environmental impacts range from trivial to positive - in those cases where the AI agents/LLMs are saving human labor - human labor and its infrastructure has enormous environmental impact too.