Anthropic Economic Index report: economic primitives
Posted by malshe 1 day ago
Comments
Comment by adverbly 1 day ago
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.
I don't really see how this is measuring the most important economic primitives. Nothing related to productivity at all actually. Everything about how and where and who... This is just demographics and usage statistics...
Comment by p1necone 1 day ago
>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.
Comment by xkcd-sucks 1 day ago
Comment by BobbyJo 15 hours ago
In my experience, "good management" meant striving to isolate measurements as much as possible to output/productivity.
Comment by brandonmenc 15 hours ago
Comment by no_wizard 15 hours ago
Comment by hazyc 1 day ago
Comment by adverbly 19 hours ago
https://ourworldindata.org/grapher/labor-productivity-per-ho...
Comment by mr_toad 15 hours ago
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.
Comment by reactordev 1 day ago
Comment by janwirth 1 day ago
Comment by reactordev 1 day ago
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.
Comment by PunchyHamster 18 hours ago
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.
Comment by reactordev 16 hours ago
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?
Comment by salynchnew 1 day ago
Comment by johnrob 1 day ago
Comment by mr_toad 15 hours ago
Comment by no_wizard 15 hours ago
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
Comment by Animats 14 hours ago
Comment by w10-1 17 hours ago
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.
Comment by amelius 20 hours ago
Comment by PunchyHamster 18 hours ago
Comment by sinnsro 18 hours ago
Comment by kurttheviking 1 day ago
Comment by verisimi 9 hours ago
Comment by fuzzfactor 1 day ago
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.
Comment by siliconc0w 1 day ago
* value seems highly concentrated in a sliver of tasks - the top ten accounting for 32%, suggesting a fat long-tail where it may be less useful/relevant.
* productivity drops to a more modest 1-1.2% productivity gain once you account for humans correcting AI failure. 1% is still plenty good, especially given the historical malaise here of only like 2% growth but it's not like industrial revolution good.
* reliability wall - 70% success rate is still problematic and we're getting down to 50% with just 2+ hours of task duration or about "15 years" of schooling in terms of complexity for API. For web-based multi-turn it's a bit better but I'd imagine that would at least partly due to task-selection bias.
Comment by storystarling 1 day ago
Comment by xiphias2 1 day ago
You can't compare the speed of AI improvements to the speed of technical improvements during the industrial revolution. ChatGPT is 3 years old.
Comment by reppap 15 hours ago
Comment by xiphias2 15 hours ago
The main difference is that people had no idea of the disruption it would cause and of course there wasn't there a huge investment industry around it.
The only question is about ROI of the investors will be positive (which depends on the timeline), not whether it is disruptive (or it will be after for example 30 years from now), and I see people confusing the two here quite often.
Comment by mlsu 1 day ago
If the output of the model depends on the intelligence of the person picking outputs out of its training corpus, is the model intelligent?
This is kind of what I don't quite understand when people talk about the models being intelligent. There's a huge blindspot, which is that the prompt entirely determines the output.
Comment by TrainedMonkey 1 day ago
Comment by mlsu 1 day ago
Comment by nl 1 day ago
In my experience with many PhDs they are just as prone to getting off track or using their pet techniques as LLMs! And many find it very hard to translate their work into everyday language too...
Comment by preciousoo 11 hours ago
Comment by chasd00 15 hours ago
Comment by Herring 1 day ago
Comment by HPsquared 1 day ago
Comment by b00ty4breakfast 1 day ago
Comment by freejazz 19 hours ago
And?
Comment by zozbot234 1 day ago
Comment by mlsu 1 day ago
These things are supposed to have intelligence on tap. I'll imagine this in a very simple way. Let's say "intellignce" is like a fluid. It's a finite thing. Intelligence is very valuable, it's the substrate for real-world problem solving that makes these things ostensibly worth trillions of dollars. Intelligence comes from interaction with the world; someone's education and experience. You spend some effort and energy feeding someone, clothing them, sending them to college. And then you get something out, which is intelligence that can create value for society.
When you are having a conversation with the AI, is the intelligence flowing out of the AI? Or is it flowing out of the human operator?
The answer to this question is extremely important. If the AI can be intelligent "on its own" without a human operator, then it will be very valuable -- feed electricity into a datacenter and out comes business value. But if a model is only intelligent as someone using it, well, the utility seems to be very harshly capped. At best it saves a bit of time, but it will never do anything novel, it will never create value on its own, independently, it will never scale beyond a 1:1 "human picking outputs".
If you must encode intelligence into the prompt to get intelligence out of the model, well, this doesn't quite look like AGI does it?
Comment by mlsu 1 day ago
You spend energy distilling the intelligence of the entire internet into a set of weights, but you still had to expend the energy to have humans create the internet first. And on top of this, in order to pick out what you want from the corpus, you have to put some energy in: first, the energy of inference, but second and far more importantly, the energy of prompting. The model is valuable because the dataset is valuable; the model output is valuable because the prompt is valuable.
So wait then, where does this exponential increase in value come from again?
Comment by felixgallo 1 day ago
Comment by retsibsi 1 day ago
I don't understand the analogy. A lever doesn't give you an increase in power (which would be a free lunch); it gives you an increase in force, in exchange for a decrease in movement. What equivalent to this tradeoff are you pointing to?
Comment by nl 1 day ago
If you ask a sophisticated question (lots of clauses, college reading level or above) it will respond in kind.
You are basically moving where the generation happens in the latent space. By asking in a sophisticated way you are moving the latent space away from say children's books and towards say PhD dissertations.
Comment by aisuxmorethanhn 1 day ago
Comment by blackqueeriroh 16 hours ago
Come on, this is human behavior 101, y’all.
Comment by wat10000 1 day ago
Comment by thousand_nights 1 day ago
you could argue that our input (senses) entirely define the output (thoughts, muscle movements, etc)
Comment by HPsquared 1 day ago
Comment by fuzzfactor 1 day ago
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Comment by dingdingdang 1 day ago
Comment by bix6 1 day ago
I just skimmed but is there any manual verification / human statistical analysis done on this or we just taking Claude’s word for it?
Comment by sdwr 1 day ago
Comment by bicepjai 9 hours ago
It also made me notice how much attention I’ve been giving these tech companies, almost as a substitute for the social media I try to avoid. I remember being genuinely excited for new posts on distill.pub the way I’d get excited for a new 3Blue1Brown or Veritasium video. These days, though, most of what I see feels like fingers-tired-from-scrolling marketing copy, and I can’t bring myself to care.
Comment by ossa-ma 1 day ago
> a sustained increase of 1.0 percentage point per year for the next ten years would return US productivity growth to rates that prevailed in the late 1990s and early 2000s
What can it be compared to? Is it on the same level of productivity growth as computers? The internet? Sliced bread?
Comment by mips_avatar 1 day ago
Comment by doganugurlu 1 day ago
Comment by malshe 1 day ago
Comment by brap 1 day ago
We get it guys the very scary future is here any minute now and you’re the only ones taking it super seriously and responsibly and benevolently. That’s great. Now please just build the damn thing
Comment by ossa-ma 1 day ago
Comment by futuraperdita 1 day ago
Note the papers cited are nearly all ones about AI use, and align more closely with management case studies vs. economics.
Comment by brap 1 day ago
Comment by blibble 1 day ago
oh I know this one!
it's created mountains of systemic risk for absolutely no payoff whatsoever!
Comment by andy_xor_andrew 1 day ago
I would never make the argument that there are no risks. But there's also no way you can make the argument there are no payoffs!
Comment by blibble 1 day ago
I think this probably says more about you than the "AI"
Comment by steve_adams_86 1 day ago