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I personally don't really get this. _So much_ work in the 'services' industries globally comes down to really a human transposing data from one Excel sheet to

I personally don't really get this. _So much_ work in the 'services' industries globally comes down to really a human transposing data from one Excel sheet to another (or from a CRM/emails to Excel), manually. Every (or nearly every) enterprise scale company will have hundreds if not thousands of FTEs doing this kind of work day in day out - often with a lot of it outsourced. I would guess that for every 1 software engineer there are 100 people doing this kind of 'manual data pipelining'. So really for giant value to be created out of LLMs you do not need them to be incredible at OCaml. They just need to ~outperform humans on Excel. Where I do think MCP really helps is that you can connect all these systems together easily, and a lot of the errors in this kind of work came from trying to pass the entire 'task' in context. If you can take an email via MCP, extract some data out and put it into a CRM (again via MCP) a row at a time the hallucination rate is very low IME. I would say at least a junior overworked human level. Perhaps this was the point of the article, but non-determinism is not an issue for these kind of use cases, given all the humans involved are not deterministic either. We can build systems and processes to help enforce quality on non deterministic (eg: human) systems. Finally, I've followed crypto closely and also LLMs closely. They do not seem to be similar in terms of utility and adoption. The closest thing I can recall is smartphone adoption. A lot of my non technical friends didn't think/want a smartphone when the iPhone first came out. Within a few years, all of them have them. Similar with LLMs. Virtually all of my non technical friends use it now for incredibly varied use cases.

deepsquirrelnet 1y ago
Making a comparison to crypto is lazy criticism. It’s not even worth validating. It’s people who want to take the negative vibe from crypto and repurpose it. The two technologies have nothing to do with each other, and therefore there’s clearly no reason to make comparative technical assessments between them. That said, the social response is a trend of tech worship that I suspect many engineers who have been around the block are weary of. It’s easy to find unrealistic claims, the worst coming from the CEOs of AI companies. At the same time, a LOT of people are practically computer illiterate. I can only imagine how exciting it must seem to people who have very limited exposure to even basic automation. And the whole “talking computer” we’ve all become accustomed to seeing in science fiction is pretty much becoming reality. There’s a world of takes in there. It’s wild. I worked in ML and NLP several years before AI. What’s most striking to me is that this is way more mainstream than anything that has ever happened in the field. And with that comes a lot of inexperience in designing with statistical inference. It’s going to be the Wild West for a while — in opinions, in successful implementation, in learning how to form realistic project ideas. Look at it this way: now your friend with a novel app idea can be told to do it themselves. That’s at least a win for everyone.
- ysofunny 1y ago
  wrong. ultimately, crypto is information science. mathematically, cryptography, compression, and so on (data transmission) are all the "same" problem. LLMs compress knowledge, not just data, and they do it in a lossy way. traditional information science work is all about dealing with lossless data in a highly lossy world.
  - danielbln 1y ago
    And it's all powered by electricity. Coincidence? I think not.
- TeMPOraL 1y ago
  > Look at it this way: now your friend with a novel app idea can be told to do it themselves. That’s at least a win for everyone. For now, anyways. Thing is, that friend now also has a reasonable shot at succeeding in doing it themselves. It'll take some more time for people to fully internalize it. But let's not forget that there's a chunk of this industry that's basically building apps for people with "novel app ideas" that have some money but run out of friends to pester. LLMs are going to eat a chunk out of that business quite soon.
marinmania 1y ago
>I would guess that for every 1 software engineer there are 100 people doing this kind of 'manual data pipelining'. For what time of company is this true? I really would like someone to just do a census of 500 white collar jobs and categorize them all. Anything that is truly automatic has already been automated away. I do think AI will cause a lot of disruption, but very skeptical of the view that most people with white collar jobs are just "email jobs" or data entry. That doesn't fit my experience at all, and I've worked at some large bureaucratic companies that people here would claim are stuck in the past.
saulpw 1y ago
Each FTE doing that manual data pipelining work is also validating that work, and they have a quasi-legal responsibility to do their job correctly and on time. They may have substantial emotional investment in the company, whether survival instinct to not be fired, or ambition to overperform, or ethics and sense to report a rogue manager through alternate channels. An LLM won't call other nodes in the organization to check when it sees that the value is unreasonable for some out-of-context reason, like yesterday was a one-time-only bank holiday and so the value should be 0. *It can be absolutely be worth an FTE salary to make sure these numbers are accurate.* And for there to be a person to blame/fire/imprison if they aren't accurate.
- samrus 1y ago
  You are correct that review and validation should still be manual. But the actual "translation" from one format to another should be automated with llms
  - zeta0134 1y ago
    You seem to be missing that the translation part isn't the expensive part, it's the validation and review. Separately, maybe this is just me, but having data actually flow through my hands is necessary for full comprehension. Just skimming an automated result, my brain doesn't actually process like half of that data. Making the process more efficient in this way can make my actual review performance *much worse.* The "inefficient" process forcing me to slow down and think can be a feature.
    - martinald 1y ago
      Not for all jobs though? There are many (imo) soul destroying 'translation' jobs at many private (and I suspect especially, public) sector companies. Think of things like typing up (scanned) paper submissions to your local government. This will often be a giant excel spreadsheet or if you are lucky something like Microsoft Access. They are absolutely riddled with mistakes as is with humans in the loop. I think this is one of the core issues with HNers evaluating LLMs. I'm not entirely some of them have ever seen how ramshackle 90%+ of operations are.
lottin 1y ago
You're vastly underestimating the complexity of these types of jobs.

deepsquirrelnet 1y ago

Making a comparison to crypto is lazy criticism. It’s not even worth validating. It’s people who want to take the negative vibe from crypto and repurpose it. The two technologies have nothing to do with each other, and therefore there’s clearly no reason to make comparative technical assessments between them. That said, the social response is a trend of tech worship that I suspect many engineers who have been around the block are weary of. It’s easy to find unrealistic claims, the worst coming from the CEOs of AI companies. At the same time, a LOT of people are practically computer illiterate. I can only imagine how exciting it must seem to people who have very limited exposure to even basic automation. And the whole “talking computer” we’ve all become accustomed to seeing in science fiction is pretty much becoming reality. There’s a world of takes in there. It’s wild. I worked in ML and NLP several years before AI. What’s most striking to me is that this is way more mainstream than anything that has ever happened in the field. And with that comes a lot of inexperience in designing with statistical inference. It’s going to be the Wild West for a while — in opinions, in successful implementation, in learning how to form realistic project ideas. Look at it this way: now your friend with a novel app idea can be told to do it themselves. That’s at least a win for everyone.

ysofunny 1y ago
wrong. ultimately, crypto is information science. mathematically, cryptography, compression, and so on (data transmission) are all the "same" problem. LLMs compress knowledge, not just data, and they do it in a lossy way. traditional information science work is all about dealing with lossless data in a highly lossy world.
- danielbln 1y ago
  And it's all powered by electricity. Coincidence? I think not.
TeMPOraL 1y ago
> Look at it this way: now your friend with a novel app idea can be told to do it themselves. That’s at least a win for everyone. For now, anyways. Thing is, that friend now also has a reasonable shot at succeeding in doing it themselves. It'll take some more time for people to fully internalize it. But let's not forget that there's a chunk of this industry that's basically building apps for people with "novel app ideas" that have some money but run out of friends to pester. LLMs are going to eat a chunk out of that business quite soon.

marinmania 1y ago

>I would guess that for every 1 software engineer there are 100 people doing this kind of 'manual data pipelining'. For what time of company is this true? I really would like someone to just do a census of 500 white collar jobs and categorize them all. Anything that is truly automatic has already been automated away. I do think AI will cause a lot of disruption, but very skeptical of the view that most people with white collar jobs are just "email jobs" or data entry. That doesn't fit my experience at all, and I've worked at some large bureaucratic companies that people here would claim are stuck in the past.

saulpw 1y ago

Each FTE doing that manual data pipelining work is also validating that work, and they have a quasi-legal responsibility to do their job correctly and on time. They may have substantial emotional investment in the company, whether survival instinct to not be fired, or ambition to overperform, or ethics and sense to report a rogue manager through alternate channels. An LLM won't call other nodes in the organization to check when it sees that the value is unreasonable for some out-of-context reason, like yesterday was a one-time-only bank holiday and so the value should be 0. *It can be absolutely be worth an FTE salary to make sure these numbers are accurate.* And for there to be a person to blame/fire/imprison if they aren't accurate.

samrus 1y ago
You are correct that review and validation should still be manual. But the actual "translation" from one format to another should be automated with llms
- zeta0134 1y ago
  You seem to be missing that the translation part isn't the expensive part, it's the validation and review. Separately, maybe this is just me, but having data actually flow through my hands is necessary for full comprehension. Just skimming an automated result, my brain doesn't actually process like half of that data. Making the process more efficient in this way can make my actual review performance *much worse.* The "inefficient" process forcing me to slow down and think can be a feature.
  - martinald 1y ago
    Not for all jobs though? There are many (imo) soul destroying 'translation' jobs at many private (and I suspect especially, public) sector companies. Think of things like typing up (scanned) paper submissions to your local government. This will often be a giant excel spreadsheet or if you are lucky something like Microsoft Access. They are absolutely riddled with mistakes as is with humans in the loop. I think this is one of the core issues with HNers evaluating LLMs. I'm not entirely some of them have ever seen how ramshackle 90%+ of operations are.

lottin 1y ago

You're vastly underestimating the complexity of these types of jobs.