Peter McCrory

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.

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,

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,