In a culture where pointless busywork is seen as mandatory to appear proper, people will eventually automate it.

https://x.com/TechEmails

I hope so, because I hope this will lead to making it OK to skip the manual encoding process. After all, AI isn't doing anything new here - it's automating the customary need for communication to be in wordy prose paying right respects to right people. Maybe people will finally see that this - not AI, but the wordy prose part - is the bullshit that helps neither the sender nor the recipient, and it'll finally become culturally acceptable to send information-dense bullet point lists in e-mails instead.

If a lawyer or am academic wrote it, it most often comes off as overly complex wording to prove a point of intellect or superiority.

For basically anyone else, when I see prose like that it most often reads like (a) they either don't understand the topic well or (b) they don't need to write it at all other than to stay busy and/or appear more valuable in a role.

I went to a career fair recently and a new grad sent me an email afterwards. His three-paragraph email can basically be reduced to "I want to work for this company. I am qualified. Please hire me". But I don't know how I would feel about that if someone actually sent me an email like this.

For the career fair grad you mentioned, a couple paragraphs would have been my expectation - three paragraphs isn't off base. If I were them I'd want to say I want to work for you, but I'd also want to at least mention why I want to work there and why you'd want me there. To me that extra context is a better email than just saying "hire me" in one or two sentences.

Personally, I don't appreciate verbosity while reading blog or news articles.

However, I don't think its bullshit. If a author is sharing, they likely are doing so in a manner that they find enjoyable.

The way I see it, you don't need to read communications like this if you don't enjoy it. Much of what gets said repeats what has been said elsewhere.

- Such e-mails are in many ways similar to a comment thread like this -> my reply is a valid example of my point.

- I wrote the comment the way I actually think about this topic. Would it be better if expanded to full-blown prose?

- Alas, this doesn't fly at work (and rarely on Internet).

Not accomplishing anything would be better than what is actually happening. Like with the hole example, once you fill it back up there’s a good chance you can still tell a hole was dug in that place.

What does “BNP” stand for in this context?

Does it raise GDP, though? I would have thought a more accurate thing to say is it raises the global temperature.

Maybe they were using Grok :-)

The method used to calculate the investment can affect whether the income produced increase the GDP or whether only the consumption generated by that increased income is counted, but in a real-world scenario either alternative will increase the GDP.

> But perhaps you are implying GDP is not correctly calculated?

That GDP doesn't accurately reflect productive, useful effort for this reason has been a core part of the criticism of GDP since it was first formulated.

The only case where digging and filling a hole does not increase GDP is if the labour is not paid for.

EDIT: Basically, the two methods you list are the income or expenditure ways of calculating GDP, but in both cases consumption by employers is a factor, and so the payment for the labour increases the GDP irrespective of whether they also increase the final output.

> the production approach estimates the total value of economic output and deducts the cost of intermediate goods that are consumed in the process (like those of materials and services)[1]

This is a very rough definition of it, but role with it. There is no economic value since the hole was dug only to be filled back in. There was a service paid for on each end of the project, but those are services that could fall into the category of intermediate goods consumed that is actually deducted. The transaction could actually have a negative GDP when using the production calculation approach.

[1] the production approach estimates the total value of economic output and deducts the cost of intermediate goods that are consumed in the process (like those of materials and services)

You can make an argument that if the hypothetical workers are salaried they're not technically paid for any given task, while I'd argue that there was an opportunity cost (they could have done other work than digging/filling it in), so there's some subjectivity to it.

My stance is that if it was done as part of paid work, they were paid to carry out the task as there's at least in aggregate if not per every one individual event an opportunity cost in having them do that work instead of something else, and so part of their consumption was paid for by the labour for those tasks, and hence they affect GDP.

That the output does not add value for the procurer of that labour does not nullify the expenditure on that labour. Whether you're calculating GDP on income or expenditure, those add to GDP either as income for the workers or an expenditure for the employer.

I'm not sold on tying it back to opportunity cost though. That may require knowing the potential value of work that could have been done instead. It also means that we could view GDP as the potential economic value if everything is optimized, regardless of what is actually produced. That feels wrong to me at first glance but I'd have to really dig into it further to have a more clear argument why.

With respect to the opportunity cost, the point is not being able to quantity it, but that whether or not the task is productive, because it takes time, it has a cost.

That blurs the line between the different calculation methods though, doesn't it? If nothing is produced then the production method of calculating wouldn't account for the transaction.

This method would also open the possibility for fraud. If the government wanted to boost GDP, for example, they could hire a bunch of people to dig a whole and fill it in all year. Would they? Probably not, they have easier ways to waste money and game GDP. But they could and that seems like a problem.

> because it takes time, it has a cost.

I don't know of any economic metrics that quantify the cost of time like this though. People like to point to unpaid labor as a huge blindspot for GDP precisely because of that - when your day is spent taking care of your home, children, or elderly parents the time is spent but GDP isn't impacted.

Buuut, then, still, my significant other loves watching Friends, which was released she was born, and is not rewinding. So it depends.

For "how do I write a bash script that will do X" the AI summary currently is way better than scanning a handful of StackOverflow tabs, already.

It will be interesting to see how "fresh" things like that stay in the world of newer or evolving programming languages. This is one of the areas where I already see the most issues (methods that no longer exist, etc).

Do we really want that though? As soon as these systems can reason through software problems and code novel solutions, there is no need for humans to be involved.

Likely we couldn't be involved at all, those systems would come up with solutions we likely would have a hard time comprehending and it would be totally reasonable for the system to create its own programming language and coding conventions that work better for it when the constraint of human readability is removed.

So at least for legal documents this LLM craze is a big improvement! It is much harder to out-spend other people on LAWYER stuff now.

A couple of years prepping cases and docs for an attorney helped me appreciate the necessity of language in law.

Conversely, the lack of legal language gives us overly broad (abusable) laws. The CFAA is one notorious example.

That tends to end up verbose.

In the case of other realms, it's just padding because previously word-count was a metric used as a proxy for quality or depth of analysis.

That level of pedanticism elsewhere, though, is incredibly annoying or precisely what is not desired.

https://www.legislation.gov.uk/ukpga/1990/18/section/1

Or if you're more patient, the whole lot:

https://www.legislation.gov.uk/ukpga/1990/18/crossheading/co...

It's all bullet points.

Is this not what happens in case law?

An LLM can probably help you understand the document if you're using it side by side with the real thing, but in this context it sounds more like you're using it to summarize.

In other words it's a cargo cult.

LLMs are more general purpose, and will probably eat/merge with that business.

ah yes the reverse autoencoder

> We are increasingly finding ourselves in a peculiar situation where the use of artificial intelligence is creating an ironic cycle of excess and reduction. On one side, AI is being employed to generate content that is often overly verbose and bloated, as algorithms churn out text that fills space rather than conveying concise or meaningful information. This output, while perhaps technically impressive in its sheer volume, often fails to serve the core purpose of clear and direct communication. It may contain a great deal of data, but much of it is irrelevant or overly embellished, making it difficult for the reader to extract anything of value. Essentially, the AI is tasked with expanding ideas into sprawling narratives or articles that only obscure the original intent.

> On the other hand, there are those who are now turning to AI to reverse this inflation, trying to distill these bloated pieces of content into more digestible, efficient versions. These AI-driven tools aim to compress the original text, stripping away the superfluous language and presenting a more focused, streamlined summary that more closely reflects the essence of the original prompt. However, this approach often feels like a futile exercise in trimming down something that was never necessary in the first place. The irony lies in the fact that the only parts people ever truly cared about—the core ideas, the relevant insights, the key messages—were buried under layers of unnecessary verbiage in the first place, only to be painstakingly uncovered and reorganized by another layer of AI intervention.

> In a sense, this back-and-forth process resembles an endless cycle of creation and destruction, where one person pays an AI to dig a hole, and another pays it to fill the hole back in. The end result may look like progress on paper—content is created, then refined, revised, and streamlined—but in reality, very little of substance is actually achieved. The net effect is often minimal, with people endlessly tweaking and refining information, but ultimately not advancing the core objective of clear communication or meaningful progress. This cycle may inflate the BNP (bloat-and-purge narrative process) without producing any tangible results, leaving us with more text, more noise, and less clarity than we had before.

And reduced again:

> The current trend sees AI generating bloated, verbose content that others then compress back into useful summaries, creating an endless cycle of inflation and reduction that accomplishes little beyond adding noise and complexity to what was originally a simple idea.

Dead Internet Theory in a nutshell.

https://en.wikipedia.org/wiki/Dead_Internet_theory#:~:text=T....