> I expected to see measures of the economic productivity generated as a result of artificial intelligence use.

>Instead, what I'm seeing is measures of artificial intelligence use.

Fun fact: this is also how most large companies are measuring their productivity increases from AI usage ;), alongside asking employees to tell them how much faster AI is making them while simultaneously telling them they're expected to go faster with AI.

When your OKRs for the past year include "internal adoption of ai tools"

It is weird right? I don't remember any other time in my career where I've been measured based on how I'm doing the work.

In my experience, "good management" meant striving to isolate measurements as much as possible to output/productivity.

The generous interpretation is that it's meant to incentivize "carpenters who refuse to use power tools" for their own good.

productivity is such a nebulous concept in knowledge work - an amalgamation of mostly-qualitative measures that get baked into quantitative measures that are mostly just bad data

economic productivity is absolutely not nebulous. Its a measure of GDP per hour worked.

https://ourworldindata.org/grapher/labor-productivity-per-ho...

And in a business you can easily measure total profit and divide by total hours worked.

When you try and break it down to various products and cost centers is where it comes unstuck. It’s hard to impossible to measure the productivity of various teams contributing to one product, let alone a range of different products.

You can thank agile for that

You don't seem to like agile, whatever that word even means.

On the contrary. I like agile for when you don’t know exactly what you’re building but you can react quickly to change and try to capture it.

Moving fast and breaking things, agile.

On the other hand. When you know what you want to build but it’s a very large endeavor that takes careful planning and coordination across departments, traditional waterfall method still works best.

You can break that down into an agile-fall process with SAFe and Scrum of Scrums and all that PM mumbo jumbo if you need to. Or just kanban it.

In the end it’s just a mode of working.

Knowing exactly what you want to build is pretty rare and is pretty much limited to "rewriting existing system" or some pretty narrow set of projects

In general, delaying infrastructure decisions as much as possible in process usually yields better infrastructure because the farther you are the more knowledge you have about the problem.

...that being said I do dislike how agile gets used as excuse for not doing any planning where you really should and have enough information to at least pick direction.

If someone comes to you and says: "I want to build a platform that does WhizzyWhatsIt for my customers, it has to be on AWS so it's mingled with my existing infrastructure. It needs to provide an admin portal so that I can set WhizzyWhatsIt prices and watch user acquisition make my bank account go brrrrrtt. It needs the ability for my quazi-illegal affiliate marketing ring to be able to whitelabel and brand it as their own for a cut of the profits."

This is obviously satire but there's a clear ask, some features, from there you know what you need to have to even achieve those features, what project management process would you employ? Agile? Waterfall? Agile-fall? Kanban? Call me in 6 months?

Probably waterfall stuff that have actual clear functions and integrations (if you can extract all that system gets and what it does with it there is no reason to agile it) then slowly get thru the current mess, documenting it at each step while trying to replace it with something better.

Replacing existing system (and especially one you didn't write) is pretty much always the hardest case.

Nice way to make all that data meaningless. I already know some people who’s jobs have pushed adoption of AI tools and it’s clear that whether or not it meaningfully impacts their speed it is not going to do them any favors to say it doesn’t even when it does not

So.... motivated reasoning makes the world go 'round?

https://en.wikipedia.org/wiki/Motivated_reasoning

Until AI is used to generate new revenue streams (i.e. acquire new customers), I don’t think the economic impact is going to impress. My two cents.

People used to say the same things about computers. Even back in the early 90s people still questioned the value of computers in the workplace.

What people, exactly? You could see the introduction of desktop computing and other types of computing in industry with a double digit increase to productivity, all other things being equal.

Any organization that properly adopted computers found out quickly how much they could improve productivity. The limiting factor was always understanding.

The trouble with AI tools is they don’t have this trajectory. You can be very versed on using them well, know all the best practices and where they apply and you get at best uneven gains. This is not the introduction of desktops 2.0

This is more like an internal marketing study. Nothing wrong with that, but it's being hyped as more than that.

"wantin' ain't gettin'": you might find productivity more important, but they didn't sign up for that.

They define primitives as "simple, foundational measures of how Claude is used". They're not signing up to measure productivity, which would combine usage with displacement, work factoring, and a whole host of things outside their data pool.

What's the point? They're offering details on usage patterns relative to demographics that can help people assessing Anthropic's business and the utility of LLM-based AI. Notably, tasks and usage are concentrated in some industries (notably software) and localities (mainly correlated with GDP and the Gini index). This enables readers to project how much usage growth can be expected.

As far as I know, no one publicly offers this level of data on their emerging businesses - not google, ebay, apple, microsoft, amazon, nvidia or any of the many companies that have reshaped our technical and economic landscape in the last 30 years.

Normally we measure value with price and overall market (productivity gains is but one way that clients can recoup their price paid). But during this the build-out of AI, investors (of all stripes) are subsidizing costs to get share, so until we have stable competitive markets for AI services, value is an open question.

But it's clear some businesses feel that AI could be a strategic benefit, and they don't want to be left behind. So there is a stampede, as reflected in the neck-and-neck utilization of chat vs API.

Reframing the meaning of words, in a way that your product's usages becomes the metric - eg so "economy" = "Claude" - makes this some sort of 'Claude promo pack'.

agree, was similarly hoping for something akin to a total factor productivity argument

I expected a simulation of the economy using economic primitives and AI.

I think we can surmise how bad that looked from the omission..

I wonder if it is even possible to get such measurements. With so many things affecting output, how can one establish a baseline or avoiding to compare apples to oranges?

the papers I've seen that tried just picked a group of developers at supposedly similar level and gave them tasks to do with or without being assisted by AI.

At the very least for programming the decline of improvement compared to difficulty of task and skill of programmer quickly got into "AI technically helps but time spent fudging with prompts is more than time saved".

However if your task is to write some SEO slop about your products for your company's webpage, or to make a bunch of business emails to customers, I can absolutely believe AI can double productivity there, there is nothing of value produced that requires skill, it's just a few bits of info filled with fluff around them

>expected to see measures of the economic productivity

I know what you mean.

Imagine my disappointment when I was expecting their unique approach and brainpower to have arrived at a straightforward index of overall world macroeconomic conditions rather than an internal corporate outlook for AI alone.