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The Anthropic Economic Index

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Re: The Anthropic Economic Index

#191

Post by psytrancefan »

groby_b wrote: post #124
frankfrank13 wrote: post #92

This more or less confirms what I imagine most of us thought, AI is mostly used by engineers, or for engineering tasks. Makes sense, I wonder how much traffic comes from automated tasks (co-pilot, etc). Every time I read a report like this I do wonder if we'll ever see an ROI on LLMs. HUNDREDS of billions of dollars of spend, and 3 years in its still primarily the same crowd using it, and has yet to create a "killer"…

Claude is mostly used by software engineers. That's an important distinction to make. I love Claude, but let's not ignore that in the LLM race, they're not exactly the leading player.

I would be pretty shocked if the Anthropic reasoning model is not mind blowing and doesn't take the lead back.

Re: The Anthropic Economic Index

#192

Post by psytrancefan »

throwaway2037 wrote: post #131
groby_b wrote: post #124

Earlier quoted context omitted.

Claude is mostly used by software engineers. That's an important distinction to make. I love Claude, but let's not ignore that in the LLM race, they're not exactly the leading player.

Can I ask a dumb question as an LLM newbie? What is it about Claude that makes it so good at basic software engineering tasks? Do you think it was finely tuned to be good at these tasks? No joke/trolling: A bunch of people have posted on HN in the last 6 months about creating MVPs (Minimum Viable Products) -- usually web apps -- using Claude. As a non-web-app programmer, I think this is amazing progress!

IMO it was just the strongest model for awhile for programming. It got the answers right more often than not.

It is faster than the reasoning/chain of thought models. With current o1 and DeepSeek though I haven't logged into Claude in a few weeks.

I have no inside knowledge but I am kind of expecting Sonnet chain of thought any day now and I am sure that will be incredible.

Re: The Anthropic Economic Index

#193

Post by sanxiyn »

Cheer2171 wrote: post #161
falafels wrote: post #152

Earlier quoted context omitted.

I listened to an interview a while back with the researchers did this analysis. They developed privacy-preserving techniques, avoiding having to read user conversations directly. This is Anthropic we're talking about, they're rightly recognized as the 'ethical' AI company.

Words I typed into a service I paid for are now in a huggingface repo. Fuck that.

They aren't.

Re: The Anthropic Economic Index

#194

Post by esperent »

steveBK123 wrote: post #119
frankfrank13 wrote: post #92

This more or less confirms what I imagine most of us thought, AI is mostly used by engineers, or for engineering tasks. Makes sense, I wonder how much traffic comes from automated tasks (co-pilot, etc). Every time I read a report like this I do wonder if we'll ever see an ROI on LLMs. HUNDREDS of billions of dollars of spend, and 3 years in its still primarily the same crowd using it, and has yet to create a "killer"…

I think the problem is the data. Software engineering is a weird niche that is both a high paying job and something you can almost self-teach from widely available free online content. If not self-teach, you can rely on free online content for troubleshooting, examples, etc. A lot of other industries/jobs are more of an apprenticeship model, with little data and even less freely available on open internet.

> something you can almost self-teach from widely available free online content.

I think you massively underestimate just how much data is online for everything, especially once you include books which are freely available on every possible subject (illegally, perhaps, but if Meta can download them for free then so can everyone else).

There's less noise for many other subjects than for software engineering, there's often just a couple rather than 100s of competing ways to do everything. There might just be one coursebook rather than 1000s of tutorials. But the data for self teaching is absolutely there.

Re: The Anthropic Economic Index

#195

Post by SilasX »

AnEro wrote: post #26

Everyone in the comments seem to not like the article or see it as a waste of time. I just don't think we are the audience they wanted for this, I think they want to show the average business owner the realistic potential and public (journalists that will distill this later) they are aware of the impacts and what to expect. I don't read it as fear AI, I read change is happening because of AI.

I honestly don’t know who the audience could be, other than “people who like to tell others they’re in the know because they read AI companies’ press releases.”

At no point do I see an actual elevator pitch/tl;dr/summary of what the frak this index actually is, except that it’s part of some effort to track AI adoption. It just rains down figures about which industries are using how much AI without first grounding the new concept they’re introducing.

When you say you have a new economic index, you need to give me a number, how I should interpret that number, and where it comes from. I don’t see that.

GDP: measure if a country’s total economic output by adding up end product purchases.

CPI: general price level by taking a weighted average of prices throughout the economy

Big Mac index: how expensive goods are in a country relative to the US by reference to the local cost of a Big Mac, converted through the exchange rate.

Here I expect something like “the economic output-weighted fraction of production taken over by AI”, but instead it’s just a list of AI adoption by industry.

Why introduce an index and not headline with a definition of an index? Which audience prefers that?

Re: The Anthropic Economic Index

#196

Post by achierius »

bakuninsbart wrote: post #136
frankfrank13 wrote: post #92

This more or less confirms what I imagine most of us thought, AI is mostly used by engineers, or for engineering tasks. Makes sense, I wonder how much traffic comes from automated tasks (co-pilot, etc). Every time I read a report like this I do wonder if we'll ever see an ROI on LLMs. HUNDREDS of billions of dollars of spend, and 3 years in its still primarily the same crowd using it, and has yet to create a "killer"…

I would agree that the products coming out so far lack imagination, but hard disagree on the impact. LLMs have completely transformed multiple industries already. In SWE, I would estimate that junior positions shrank by 70-80%, but even that is less extreme than what is going on in other industries. In marketing, the entire low-end to mid-tier market is gone. Instead of having teams working on projects for small to m…

> I would estimate that junior positions shrank by 70-80%

This just isn't true, it's nowhere close.

Re: The Anthropic Economic Index

#197

Post by BeetleB »

esperent wrote: post #194
steveBK123 wrote: post #119

Earlier quoted context omitted.

I think the problem is the data. Software engineering is a weird niche that is both a high paying job and something you can almost self-teach from widely available free online content. If not self-teach, you can rely on free online content for troubleshooting, examples, etc. A lot of other industries/jobs are more of an apprenticeship model, with little data and even less freely available on open internet.

> something you can almost self-teach from widely available free online content. I think you massively underestimate just how much data is online for everything , especially once you include books which are freely available on every possible subject (illegally, perhaps, but if Meta can download them for free then so can everyone else). There's less noise for many other subjects than for software engineering, there's…

The difference is in the cost of the equipment.

Sure, you can learn all about power electronics by yourself. But have some ideas you want to implement? Hundreds to tens of thousands of dollars.

Re: The Anthropic Economic Index

#198

Post by BeetleB »

kanbankaren wrote: post #127
steveBK123 wrote: post #119

Earlier quoted context omitted.

I think the problem is the data. Software engineering is a weird niche that is both a high paying job and something you can almost self-teach from widely available free online content. If not self-teach, you can rely on free online content for troubleshooting, examples, etc. A lot of other industries/jobs are more of an apprenticeship model, with little data and even less freely available on open internet.

> Software engineering is a weird niche that is both a high paying job and something you can almost self-teach If you meant programming, I agree it could be self-taught, but not SE. SE is the set of techniques, practices, tools that we have collected over decades for producing multi-versioned software that meets a certain reliability rating. Not all of these is freely available online.

I'll bite. Can you list specific things not freely available online?

Re: The Anthropic Economic Index

#199

Post by satellite2 »

esperent wrote: post #194
steveBK123 wrote: post #119

Earlier quoted context omitted.

I think the problem is the data. Software engineering is a weird niche that is both a high paying job and something you can almost self-teach from widely available free online content. If not self-teach, you can rely on free online content for troubleshooting, examples, etc. A lot of other industries/jobs are more of an apprenticeship model, with little data and even less freely available on open internet.

> something you can almost self-teach from widely available free online content. I think you massively underestimate just how much data is online for everything , especially once you include books which are freely available on every possible subject (illegally, perhaps, but if Meta can download them for free then so can everyone else). There's less noise for many other subjects than for software engineering, there's…

Consider two fields with vast amounts of literature: medicine and law.

Medicine faces two key challenges. First, while research follows the scientific method, much of what makes a good doctor—intuition, pattern recognition, and clinical judgment—is rarely documented. Second, medical data is highly sensitive, limiting access to real-world cases, images, and practice opportunities. Theory alone is not enough; hands-on experience is essential.

Law presents a different problem: unknown unknowns. The sheer volume of legal texts makes it nearly impossible to be sure you’ve found everything relevant. Even with search tools, gaps in knowledge remain a major risk.

Compounding this is the way law is actually practiced. Every judge and lawyer operates with a shared foundation of legal principles so basic they are almost never discussed. The real work happens at two higher levels: first, the process—how laws are applied, argued, and enforced in practice. Then, at a third, more abstract level, legal debates unfold about interpretation, precedent, and systemic implications. The first level is assumed, the second is routine, and only the third is where true discussion happens.

Self-teaching is easier in fields where knowledge is structured, accessible, and complete. Many subjects are not.

Re: The Anthropic Economic Index

#200

Post by ggm »

bufferoverflow wrote: post #126
fragmede wrote: post #111

Earlier quoted context omitted.

Yeah. reminds me of the ancient okcupid data analysis blogs and not the creepy one by sleep8. The group I'm surprised not to see represented in their analysis is "personal", where people I know use ChatGPT as a therapist/life coach/sms analysis&editor. and of course they crucially but understandably left off the denominator. 35% of a million requests is different than 35% of a billion. and also how many of the conver…

> 35% of a million requests is different than 35% of a billion. Not statistically.

35% of a million students in the USA is very different to 35% of a billion students across the USA, Europe and Africa.

Since there aren't a billion students in the USA, 35% of them is an impossibility.

If you scale your population above some recognized boundary you aren't sampling in the same space any more. After all the local star density to 1AU tends very strongly to 1. That's not indicative of the actual star density in the milky way.

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