When employees think about a guy, do they think about lifting the bar, then
unleashing creativity or is it something like, am I going to lose my job?
You know, the answer is both, but it turns out that their positivity actually
outweighs their fears right now. And that's those are some of the
findings from our most recent report that we published yesterday.
Just back up for a second. Thirty one thousand people across 31
countries we surveyed to understand how they felt.
First off, the number that caught my attention.
64 percent of people telling us that they just don't have the time or energy
to get their jobs done. When we looked at Asia PAC,
specifically, that number jumped up to 72 percent.
So there's something going on there. Then we asked them how they felt about
A.I.. Forty nine percent did say, I'm afraid
it's going to take my job. But again, when we asked them what would
you use it at work, 70 percent worldwide told us that they would offload as much
as they could in the workplace just because they felt like they were such a
burden of work right now. How much is A.I.
being incorporated in America's workplace right now?
It's early days. It's very early days and March.
We introduced a brand new product called Microsoft Co-pilot.
If this product is just in testing for us right now.
So we have initially 20 customers that have been using it.
Yesterday we announced that we're expanding to an additional 600 customers
worldwide. So we're excited to have it be used more
broadly. But it's definitely early days.
I can say from my own usage, though, I've been using it for a couple of
months. This is a product that once you get your
hands on it, you don't want to give it back.
It helps you summarize e-mails, write emails, summarize long documents.
It makes meetings, you know, just a totally different experience.
So there is a set of capabilities that once you get a taste of it, you realize
it's going to change the way you do your work.
Jared, there's so much excitement when it comes to AI at the moment.
You just have to see how concentrated investors are in the sector and in the
Chinese market, for example, what are the pitfalls and where do you see
weaknesses that still really need to be worked on before we see mainstream,
reliable, consistent application? It's a great question in these large
language models. I think the world came to know them
first last November, which beauty these large language models are pretty
incredible at answering questions across a broad domain.
But when it comes to answering business questions, specific questions about your
business, about results, about things that could help you get your job done.
The general purpose models aren't good enough.
And so what we are seeing companies do, including what we're doing with our
products, is we are grounding the large language models with additional company
data. So we take your data.
That's your emails, your calendar and your financial data.
We send it to the large language models and let them reason across it.
In those circumstances, it does quite well.
It's not perfect, however. So when we introduced in March, we did
help people understand that it still makes mistakes.
So I like to think of it as an assistant that's incredibly knowledgeable.
It's excellent that kind of going across a large body of information, but you
can't trust it on a percent. You have to check the answers it gives
you. How much consolidation?
I don't know. You obviously come from one point of
view, but given how many players that we're just talking about, Softbank, for
example, coming with its Virgin's myriad players at the moment jostling for
market share, how much consolidation do you see ultimately?
Well, the moment is an interesting moment because it's a bit of let a
thousand flowers bloom. Everyone is talking about A.I.
in the context of their applications or their Web services, their cloud services
ultimately over time. Our sense is that users are going to
gravitate toward to the use of A.I., what we'd call these co pilots that kind
of aggregate the greatest amount of information.
It's pretty tiresome to think that you'd have to go from one co-pilot to another
to another and know which one to ask the questions.
So ultimately, we think this is going to come down to user choice.
And our sense from the testing is that users are going to go to those that
really have a broad view of business. And that's what we hope to do with that,
with capturing emails, calendar, kind of a broad swath of things and then being
able to integrate management business systems.
We hope that we can give users kind of access to a large enough amount of data
that they get some really useful answers.