Azeem Azhar's Exponential View
Anthropic’s Head of Economics on AI adoption data, Claude Code, the burden of knowledge & the next generation of experts
21 Jan 2026
Chapter 1: What insights does Anthropic's Economic Index report provide?
So Anthropic, the leading AI lab behind Claude, has just released the next edition of their Economic Index report. They have analysed millions of real conversations with Claude to map exactly where AI is augmenting human work today and where it isn't. I'm with Peter McCrory, who is the head of economics at Anthropic, and he's the one who led this research.
I think it's the best empirical window we have into how AI could be sharing shaping work right now. So, Peter, thank you so much for breaking away from your computer and joining us today.
It's a privilege to be here and so glad to be able to share this work with the world and all the underlying data, which I mean, I'll talk about, but everything that we do is based on open source data. So we hope that others will join us in making sense of what's on the horizon.
You know, that is such an important point because in this moment of the investment boom and the prospect of artificial intelligence really changing the way we live, the quality of data I found has been really, really poor.
Chapter 2: How do Claude's usage patterns differ between API and chat interfaces?
It's sort of scuttlebutt and, you know, survey a few mates and slap a logo on it and change the way the market thinks. So when you get data from, you know, Anthropic or Epoch or others, it's really good to be able to hold on to it. Your data shows something quite striking, which is two completely different use patterns emerging at the same time.
So if you're using Claude, you can do it like most of us do through the chat interface, you know, tippity-tappity-tippity, Claude goes away and thinks and comes back with an answer, or
You can use it through the API, which is a programming interface, which means that perhaps you're accessing it through another piece of software, maybe one that you have written or more likely your IT department has written. So what I found really interesting is, I think your previous report showed as well,
that use cases through the API are about 75% in what you call automation of tasks, but they have lower success rates. Whereas if you look at the interactions on claw.ai, the task mix is much more around augmentation, but it's also likely to result in more success. So these aren't just two channels. They're two different stories about how AI integrates into the workplace and the future of work.
Which one is more indicative of what the future is?
That's a really great question. And I think of this in one of two ways. One, broadly speaking, the usage patterns on cloud.ai, which is this chatbot interaction, do at a high level look very similar to the API deployment.
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Chapter 3: What impact does AI have on the labor market today?
So dominant usage for coding related tasks, as well as sort of the other overrepresented categories. with a little bit more tilt toward programmatic deployment when businesses choose to embed Cloud's capabilities.
So high level, I tend to see these as both capturing where are capabilities strongest and where are they providing economic value, whether that's through sort of iterative back and forth with a user through the chat window or through the API deployment.
But to your point, so much of the, and this is a point that we made in our last report, is so much of the labor market and productivity implications of this technology, much like past technologies, will hinge on how businesses choose to embed and deploy the tool. And so the sharp increase, relative increase in automated use, where
Claude has given a straightforward directive and expected to produce an output that feeds directly into a service that's provided to a consumer or some internal business operation is where I think the productivity effects will begin to materialize.
This sort of matches sort of the historical pattern of general purpose technologies where businesses need to figure out how to maybe even embed the capability in invisible ways. So the analogy that I use is with electricity.
When I go to a coffee shop and I order a latte, I don't often think, except in this conversation, don't often think about the power of the electricity that's required to provide this service. That general purpose technology is invisible to me.
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Chapter 4: Why is extracting tacit knowledge crucial for AI adoption?
And it will take time for businesses to figure out what those use cases are, the cloud.ai usage patterns might be an early window into what's on the horizon. So early adopters use the chatbot to complete very sophisticated tasks through multi-turn interactions. Businesses learn that there's immense economic value there should they be able to provide the right contextual information.
And then over time, that gets embedded in business workflows.
We're going to investigate all those ideas like multi-step and over time. I'm going to be a bit cheeky. So when I looked at this data after your previous economic report, the way I took this away was human employees like you and I We think of ourselves and what our job entails in the round.
And so when we get a superpower tool like Claw.ai, we think about how we do that job better, make it more interesting, make it more challenging, get through not just our must-do list, but our coulds and woulds where the real value and enjoyment may lie.
Whereas it turns out that companies actually see their employees as bundles of tasks where you can discretize them and you don't ask the question, what does Peter really need to kind of manifest himself fully as the head of economics at Anthropic, which is what you think of about your job in your head? They think,
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Chapter 5: How can we cultivate the next generation of experts in AI?
What are the 17 tasks Peter does and which are the ones can we automate discreetly? And I felt it actually said something a little bit more about that different perspective, the relationality that an individual has with their own work and the honesty with which their boss actually thinks about that individual.
Yeah, I think that that's an interesting point. observation, and I think it illustrates the fact that, I mean, so one implication of this report was the sort of uneven impact that across different sorts of jobs, and the fact that some jobs are likely to be fundamentally transformed and maybe even have greater risk of displacement.
So if you take into account task reliability, like what is Claude really good at doing? And you look at what are the tasks that are very time intensive for certain types of workers. You plug that into what data we actually see on our platform. You get a nuanced picture of sort of who is exposed to AI. One example here would be sort of data entry workers.
These are tasks where in our analysis, Claude is pretty reliable at extracting information in standardized ways from sort of natural language reports.
Chapter 6: What is the significance of cognitive endurance in the age of AI?
that's the most essential task in that job. And I mean, maybe to your point, that's where it might be more straightforward for businesses to figure out how to automate that specific workflow.
In our data in this report, we actually saw a jump in office and administrative usage as a total share of our API traffic, suggesting that businesses are broadening out beyond just coding related tasks in how they choose to sort of deploy the tool into these like back office administrative support tasks. It's not obvious.
I don't think that this means that your job will become sort of less meaningful or more meaningful.
Chapter 7: How do productivity expectations differ between short-term and long-term?
but it may mean that your job fundamentally changes. For data entry workers, maybe that type of role becomes more, and this is very speculative, so you can push back, but maybe someone needs to manage more the sort of relaying of information that Claude is providing. And it's not quite a data entry worker focused on plugging in the data points from a report into a spreadsheet.
but there's nevertheless this important role that a human will play in translating and engaging with sort of people within the organization.
Well, I mean, what we're doing there is we're going back and we're playing through what happened with previous waves of general purpose technologies and automation. And I think the observation you made that maybe what people do on the chat interfaces is a little bit of an early warning about, or early signal rather than warning, about what you can do when you start to automate it. Because I think
One of the observations would be that if all you do is automate a task-like data entry that had fundamentally been gated by the marginal value of the nth item that was entered into data by a human who you're paying per hour, you had already taken a scarcity mindset to that, and you were only going to look at the minimal amount of data that you needed to achieve the outcome.
Now, if that cost drops by a factor of, say, 1,000, you're not just talking about looking at five more customer records. You're talking about looking at a thousand times more, which is a regime shift, right?
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Chapter 8: What are the potential unmeasured impacts of AI on knowledge and productivity?
Even if the fundamental kind of economic organizational model remains the same, a thousand X is a regime shift, which means new behaviors emerge. And some of the things that I see when I talk to businesses is, you know, a large part of them are doing the sorts of things we've discussed by automating data entry. But what they've done is they've automated the horse.
They've automated the fact that they used to look at 10,000 pieces of data a day by humans and now they look at 10,000 by machines rather than saying, well, what if we looked at 10 billion and how would that change our business downstream from that? I mean, you know, I feel that there's something there.
Yeah, you know, I think this sort of reminds me of one of the more... complicated insights from our last report, but I actually think is quite relevant here, which is the set of things that businesses will do over time to restructure sort of their modernizing data tech stack or new organizational workflows to unlock the productivity.
It's not just do what you had been doing before, but sort of fundamentally restructure how the business operates. The historical analog here would be, again, electricity, where factories shifted from centralizing power on the factory floor to more distributed power. That changed fundamentally how factories operated. So what do we see in the data? We actually look at
how much context do businesses, when they use Cloud through the API, how much contextual information do they provide, and how much output does the model produce? And actually, if you generate more output tokens, that tends to be the most complex tasks.
It turns out that in order to get those most complex implementations, so moving beyond just straightforward data entry to something that's much more sophisticated, maybe like automating biological research and analysis,
Oh, wow. We've taken a big step back.
Yeah, that's a big step. And, you know, that is arguably like something that is... maybe on the horizon, like I think a lot about even our productivity analysis might be conservative if we're failing to account for the automation of innovation itself. Maybe we can return to that. I'm kind of curious to hear your perspective.
But it turns out for those most complex tasks that we see in our data, businesses need to provide disproportionately more contextual information. So even if the capabilities are there, If you don't have the relevant information to deploy that capability, business adoption will maybe be constrained or bottlenecked.
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