281–290 of 783 posts
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Not a phase, I’d argue that 90% of modern jobs are bullshit to keep cattle occupied and economy rolling.
Cattle? You actually think that about other people?
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Anthropic and OpenAI are both well documented as losing billions of dollars a year because their revenue doesn't cover their R&D and training costs, but that doesn't mean their revenue doesn't cover their inference costs.
Does it matter if they can't ever stop training though? Like, this argument usually seems to imply that training is a one-off, not an ongoing process. I could save a lot of money if I stopped eating, but it'd be a short lived experiment. I'll be convinced they're actually making money when they stop asking for $30 billion funding rounds. None of that money is free! Whoever is giving them that money wants a return on…
Once that happens, whomever is left standing can dial back the training investment to whatever their share of inference can bear.
I bet many CEO PA are using AI for many tasks. It's typically a role where AI is very useful. Answering emails, moving meetings around, booking and buying a bunch of crap.
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That means the system will collapse in the future. Now from bunch of people some good programmers are made. Rest go into marketing, sales, agile or other not really technical roles. When the initial crowd will be gone there will be no experienced users of AI. Crappy inexperienced developer will make more crap without prior experience and ability to judge the design decisions. Basically no seniors without juniors.
This implies that writing code by hand will remain the best way to create software. The seniors today who have got to senior status by writing code manually will be different than seniors of tomorrow, who got to senior status using AI tools. Maybe people will become more of generalists rather than specialists.
That’s putting it mildly. I think it’s going to be interesting to see what happens when an entire generation of software developers who’ve only ever known “just ask the LLM to do it” are unleashed on the world. I think these people will have close to no understanding of how computing works on a fundamental level. Sort of like the difference between Gen-X/millenial (and earlier) developers who grew up having to interact with computers primarily through CLIs (e.g., DOS), having to at least have some understanding of memory management, low-level programming, etc. versus the Gen-Z developers who’ve only ever known computers through extremely high level interfaces like iPads.
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What if LLMs are optimizing the average office worker's productivity but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book.
> but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book. That book was very different than what I expected from all of the internet comment takes about it. The premise was really thin and did't actually support the idea that the jobs don't generate value. It was comparing to a hypothetical world where everything is perfectly organized, everyon…
I don't know if maybe he wasn't explaining it well enough, but that kind of reasoning makes some sense.
A lot of code is written because you want the output from Foo to be the input to Bar and then you need some glue to put them together. This is pretty common when Foo and Bar are made by different people. With open source, someone writes the glue code, publishes it, and then nobody else has to write it because they just use what's published.
In corporate bureaucracies, Company A writes the glue code but then doesn't publish it, so Company B which has the same problem has to write it again, but they don't publish it either. A hundred companies are then doing the work that only really needed to be done once, which makes for 100 times as much work, a 1% efficiency rate and 99 bullshit jobs.
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It’s also pretty wild to me how people still don’t really even know how to use it. On hacker news, a very tech literate place, I see people thinking modern AI models can’t generate working code. The other day in real life I was talking to a friend of mine about ChatGPT. They didn’t know you needed to turn on “thinking” to get higher quality results. This is a technical person who has worked at Amazon. You can’t expec…
I don't think "results don't match promises" is the same as "not knowing how to use it". I've been using Claude and OpenAI's latest models for the past two weeks now (probably moving at about 1000 lines of code a day, which is what I can comfortably review), and it makes subtle hard-to-find mistakes all over the place. Or it just misunderstands well known design patterns, or does something bone headed. I'm fine with…
If not, you don't know how to use it efficiently.
A large part of using AI efficiently is to significantly lower that review burden by having it do far more of the verification and cleanup itself before you even look at it.
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the greatest step change will be when mainstream business realise they can use AI to accurately fill in PDF documents with information in any format filling in pdf documents is effectively the job of millions of people around the world
That would require accurate validation of said documents, which is extremely hard now. Pointing 1 million PDF LLM machine guns at current validation pipelines will not end well, especially since LLMs are inherently unreliable.
So you lose a lot of benefits to the time sync, but since people tend to have their eye glaze over when the correction rate is low, you may still miss the 2% anyway.
This is going to put a stop to a lot of ideas that sound reasonable on paper.
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This implies that writing code by hand will remain the best way to create software. The seniors today who have got to senior status by writing code manually will be different than seniors of tomorrow, who got to senior status using AI tools. Maybe people will become more of generalists rather than specialists.
> The seniors today who have got to senior status by writing code manually will be different than seniors of tomorrow, who got to senior status using AI tools. That’s putting it mildly. I think it’s going to be interesting to see what happens when an entire generation of software developers who’ve only ever known “just ask the LLM to do it” are unleashed on the world. I think these people will have close to no unders…
Sure, maybe you would have caught the bug if you wrote assembly instead of C. But the C programmer still released much better software than you faster. By the time you shipped v1 in assembly, the C program has already iterated 100 times and found product market fit.
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So management basically have no clue and want you to figure out how to use AI? Do they also make you write your own performance review and set your own objectives?
> Do they also make you write your own performance review and set your own objectives? Not to get off on a tangent but this has got to be a "tell" for how much a company is managed by formula and how much it's actually got thinking people running things. Every time I've had to write my own review I fill out the form with some corporatese bullshit, my supervisor approves it and adds some more bullshit, it disappears i…
In my first year I didn’t know any better, so I tried to set myself some actual objectives (learn to use XYZ, improve test coverage by X%, measurable stuff that would actually help).
Fortunately my manager showed me how to do it correctly, so now my goals are to “differentiate with expertise” and to “empower through better solutions”.
Every year I open up the self-review, grade myself a 5/5 on these absurd, unmeasurable goals, my manager approves it, and it disappears off somewhere into the layers and layers of ever-higher management where nobody cares to look at it.
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operationally, i think new startups have a big advantage on setting up to be agent-first, and they might not be as good as the old human first stuff, but theyll be much cheaper and nimble for model improvements
Start ups mostly move fast skipping the necessary ceremony which large corps have to do mandatorily to prevent a billion dollar product from melting. Its possible for start ups because they don't have a billion dollar to start with. Once you do have a billion dollar product protecting it requires spending time, money and people to keep running. Because building a new one is a lot more effort than protecting existing…
Once you have revenue you have downside to protect. Pre-revenue the worst that can happen is that you have to start again knowing more than you did.