[nine_k]

>> Harvard, MIT

> people are being paid too much to spend all day emailing each other

Hmm, this does not sound exactly right. Also, does anybody seriously think that communication is not work, or is not important? A number of really impactful things started from people emailing each other. (Hell, Linux kernel development is still much about people emailing patches each other.)

[pembrook]

The problem with human labor is that, as an organization scales, the amount of work any individual in the system can do shrinks due to the coordination problem.

Coordination consumes a larger and larger amount of employee time to the point that, in the absolute largest organizations, the vast majority of employee time is internal coordination vs. actual improvement/selling of the customer offering.

So if you go from 100 employees to 1,000 employees, they can MAYBE do 4X the work. Not 10X like you'd think. And this effect gets even worse as you scale further.

So if an AI can do 10X more labor in a human day, and can coordinate instantaneously via a central context ledger (say a git repo), it doesn't just create 10X gains in productivity for large orgs. It creates a multiple of that 10X due to also removing the human coordination overhead.

[_pdp_]

Don't you think AI itself is something that adds coordination overhead? A 1000 strong team with AI agents will feel like 5000-person company where more than 30% are not even at exception level - i.e. they need to be pulled along.

This is why having less people and more agents actually makes sense but the coordination problem remains either way.

And you cannot escape it because it is simply mathematical.

[pembrook]

The coordination problem absolutely can be escaped with technology, hence why productivity gains exist and why the economy grows and isn't a fixed pie over time.

Here's an easy non-AI example:

In the past, a 'computer' was literally a person [1]. If you needed to synthesize large amounts of data, you needed to split the task among a team of people writing things down and then a team of people to check their work after the fact and then a team of people to combine all the work and then a team to double-check the combined work.

Tasks that in the past would have taken a room full of people coordinating with pencils are absolutely done by 1 machine today (what we know as computers) that no longer needs to split that task and coordinate, which is exactly what will happen with 'agents' who can take on vastly more work per unit of time.

[1] https://en.wikipedia.org/wiki/Computer_(occupation)

[_pdp_]

Look up Amdahl's Law and Universal Scalability Law.

The math doesn't care whether the nodes are people, CPUs or language models. If agent A's next action depends on what agent B decided, you've introduced a sequential dependency.

[pembrook]

The point is that we don't need an equivalent number of nodes (agents) as we needed people.

The computer flattened the coordination dependencies of that room full of people by doing all the calculations by itself. As they get smarter, you can theoretically assume 1 agent could eventually run the entire US federal government.

In the historical [human] computer example; if 15,000 calculations needed to be done, a CPU doesn't need to wait on Bob to come back from lunch to do the next 20 calculations...and doesn't need to wait on Alice to combine his work with the 20 calculations done by Jane...and doesn't need Bill to wait for everybody to be done to double check Jane's work.

The CPU does all 15,000 calculations instantly, by itself. This will be similar with AI agents.

[Nevermark]

Note that Amdahl's rule doesn't capture the practical situation.

1) The purpose of algorithms is ultimately to create value, not compute some fixed value X. This is important as it gives flexibility to choose different value producing tasks where parallelism dominates over serial tasks, whenever the the latter becomes a bottleneck.

2) In terms of producing value, perfect accuracy or the best possible solutions are not always necessary. Many serial tasks can become very parallel tasks when accuracy or certainty do not have to be complete.

3) Solutions that are reusable changes the math further. No matter how serial a calculation is, if something is calculated that can be reused, that serial part becomes effectively order O(1), after calculation if reused exactly, but as neural network demonstrate, many serial tasks become very parallelized after training a model that can be reused for now a wide class of specific problems. Resulting in very amortized serial computing costs.

It doesn't matter how many steps something takes, if those steps are now in the past and the value is "forever" reusable.

4) The economics of serial and parallel computation are not static, but improve relative to economic value achieved. Meaning that demand for cheaper serial time and currency costs result in improved scaled up hardware that delivers cheaper serial costs. This may have less impact than the previous points, but over years makes a tremendous difference on top of all those points.

This can go on.

The point being Amdahl's law certainly applies to specific algorithms, but is not the dominant determinant of computing in general, and not useful application of computing to a significant degree, where problems can be strategically chosen, strategically weakened or altered, and can be strategically fashioned to create O(V) of value - to balance any O(S) cost of serial computing, via direct reuse and generalization.

[MITSardine]

In an organization, the number of sequential steps doesn't really scale with number of participants, does it? Rather with dependent steps of the tackled process; say, devise building permit request, await approval, purchase materials, move materials to site, hire workforce, etc.

Theoretically, each of those steps is parallelizable to some extent. Amdahl's law equivalent here would be that some delays are outside the reach of an organization to improve. For instance, a building permit will take the time it takes to be examined based on an external public administration.

[amirhirsch]

Critically, Amdahl’s Law remains a conjecture until we have the maths to disjoin complexity class P from NC.

[keeda]

Yep, I call this the "Conway Overhead": https://news.ycombinator.com/item?id=47270142

[wolvoleo]

This is so true. I can see the same in our organisation. So much time wasted on bullshit busywork and red tape.

[reverius42]

Does anybody seriously think that communication is something an LLM can do for you?

(Hint: if you have to prompt it to write an email -- you could have saved everyone some time and emailed the prompt instead.)

[jazzpush2]

I have several friends at the big 4 who work ~2 hour weeks.

[ornornor]

While it might sound like the ultimate hack, it’s not.

I’ve been in that situation and died a little inside everyday. It’s not like being a rentier, because you still have to lose most of your day at the office and pretend to work, and be available in case some higher up needs something so you don’t get caught.

It might sound sweet but it’s hell.

[psjs]

Looks like Graebers bullshit jobs, right?

  1. https://davidgraeber.org/articles/on-the-phenomenon-of-bullshit-jobs-a-work-rant/
  2. https://en.wikipedia.org/wiki/Bullshit_Jobs
  3. https://theanarchistlibrary.org/library/david-graeber-bullshit-jobs

[resonious]

Completely agreed. The thought that "people emailing each other" is a problem that should be "automated" away is delusional.

[anonzzzies]

Some type of emailing is important, what most people do, however, is not. Same with meetings, calls etc. Most of it is filling the day so they don't get fired.

[Esophagus4]

This is such a broad generalization it is easy to dismiss.

[uoaei]

Yours is such an unspecific criticism it's impossible to take seriously.

[Esophagus4]

I guess we’re at a stalemate, then.