I don't see the former as that much of a problem.
You as the employer are liable, a human has real reasoning abilities and real fears about messing up, the likely hood of them doing something absurd like telling a customer that a product is 70% off and them not losing their job is effectively nil. What are you going to do with the LLM, fire it?
Data scientist and people deeply familiar with LLMs to the point that they could fine tune a model to your use case cost significantly more than a low skilled employee and depending on liability just running the LLM may be cheaper.
As an accounting firm ( one example from above ) far as I know in most jurisdictions the accountant doing the work is personally liable, who would be liable in the case of the LLM?
There is absolutely a market for LLM augmented workforces, I don't see any viable future even with SOTA models right now for flat out replacing a workforce with them.
Some people argue that it doesn’t matter if there is mistakes (it depends which actually) and with time it will cost nothing.
I argue that if we give up learning and let LLM do the assignments then what is the extent of my knowledge and value to be hired in the first place ?
We hired a developper and he did everything with chatGPT, all the code and documentation he wrote. First it was all bad because from the infinity of answers chatGPT is not pinpointing the best in every case. But does he have enough knowledge to understand what he did was bad ? And then we need people with experience that confronted themselves with hard problems and found their way out. How can we confront and critic an LLM answer otherwise ?
I feel student’s value is diluted to be at the mercy of companies providing the LLM and we might loose some critical knowledge / critical thinking in the process from the students.
However I really don't need to implement some weird algorithms myself every time (ideally I am using a well tested Library) but the point is that you learn to be able to but also to be able to modify or compose the algorithm in ways the LLM couldn't easily do.
Spotify CEO is channeling The Two Bobs from Office Space: "What are you actually doing here?" Just in a nastier way, with a kind of prisoner's dilemma on top. If you can get by with an agent, fine, you won't bother him. If you can't, why can't you? Should we replace you with someone who can, or thinks they can?
Spotify CEO is not his employees' friend.
An agent can't do it. It can help you like a calculator can help you, but it can't do it alone. So that means you've become the programmer. If you want to be the programmer, you always could have been. If that is what you want to be, why would you consider hiring anyone else to do it in the first place?
> Spotify CEO stated on X that before asking for more headcount they have to justify not being able to do the job with an agent.
It was Shopifiy, but that's just a roundabout way to say that there is a hiring freeze due to low sales (no doubt because of tariff nonsense seizing up the market). An agent, like a calculator, can only increase the productivity of a programmer. As always, you still need more programmers to perform more work than a single programmer can handle. So all they are saying is that "we can't afford to do more".
> The company will and is just using the agent as well …
In which case wouldn't they want to hire those who are experts in using agents? If they, like Shopify, have become too poor to hire people – well, you're screwed either way, aren't you? So that is moot.
Where will all those new students find a job if :
- they did not learn much because LLM did work for them
- there is no new jobs required because we are more productive ?Never in the history of humans have we been content with stagnation. The people who used to do manual calculations soon joined the ranks of people using calculators and we lapped up everything they could create.
This time around is no exception. We still have an infinite number of goals we can envision a desire for. If you could afford an infinite number of people you would still hire them. But Shopify especially is not in the greatest place right now. They've just come off the COVID wind-down and now tariffs are beating down their market further. They have to be very careful with their resources for the time being.
> - they did not learn much because LLM did work for them
If companies are using LLMs as suggested earlier, they will find jobs operating LLMs. They're well poised for it, being the utmost experts in using them.
> - there is no new jobs required because we are more productive ?
More productivity means more jobs are required. But we are entering an age where productivity is bound to be on the decline. A recession was likely inevitable anyway and the political sphere is making it all but a certainty. That is going to make finding a job hard. But for what scant few jobs remain, won't they be using LLMs?
Were they wrong? People who rely too much on a calculator don't develop strong math muscles that can be used in more advanced math. Identifying patterns in numbers and seeing when certain tricks can be used to solve a problem (verses when they just make a problem worse) is a skill that ends up being beyond their ability to develop.
Yes. People who rely too much on a calculator weren't going to be doing advanced math anyway.
Then they're on a video call and their vocabulary is wildly different, or they're very clearly a recent immigrant and struggle with basic sentence structure such that there is absolutely zero change their discussion forum persona is actually who they are.
This has happened at least once in every class, and invariably the best classes in terms of discussion and learning from other students are the ones where the people using AI to generate their answers are failed or drop the course.
If there is a difference, then fundamentally LLMs cannot solve problems for you. They can only apply transformations using already known operators. No different than a calculator, except with exponentially more built-in functions.
But I'm not sure that there is a difference. A problem is only a problem if you recognize it, and once you recognize a problem then anything else that is involved along the way towards finding a solution is merely helping you solve it. If a "problem" is solved for you, it was never a problem. So, for each statement to have any practical meaning, they must be interpreted with equivalency.
There is a measurable decrease in critical thinking skills when people consistently offload the thinking about a problem to an LLM. This is where the primary difference is between solving problems with an LLM vs having it solved for you with an LLM. And, that is cause for concern.
Two studies on impact of LLMs and generative AI on critical thinking:
https://www.mdpi.com/2075-4698/15/1/6
https://slejournal.springeropen.com/articles/10.1186/s40561-...