Lili Cheng, Corporate Vice President of Microsoft AI and Research, chats with Fortune about building vs. buying AI systems, AI copilots, and the future of the advancing technology.
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00:00Lily, I think what everyone is excited to hear about is you being on the AI front lines
00:05at Microsoft.
00:06So I want to start by talking about the big trends that you're seeing in AI models.
00:10So what new capabilities in AI systems are coming soon that all of the people here, CIOs,
00:16top tech people should really know about?
00:18Well, it was great actually talking before, you know, being up here.
00:22I think everybody's just kind of immersed in AI and how quickly things are moving.
00:28And so, you know, in a way it's hard to predict what's going to happen in the next six months
00:33even.
00:34I mean, we see a lot of trends that you've probably seen over the last week or two, you
00:38know, more types of conversation and media coming in, more asynchronous conversation
00:44and the ability to do more complex tasks.
00:49I think so far we've seen a lot of progress in some of our co-pilots at Microsoft, like
00:56the GitHub co-pilot.
00:57We've seen developers, I think today over 50% of all code in GitHub is AI generated,
01:04which is kind of mind-blowing for a product.
01:06It was one of the first co-pilots.
01:09And we see the developers, probably 90% of developers, find that they can do their repetitive
01:15tasks much more easily.
01:17But still maybe 60% or 70% of what a developer does isn't coding.
01:22It's really around collaboration and coordinating work, doing deeper thought.
01:28And so I think one of the things that you're going to see AI looking at is how do we address
01:32some of the real challenges that people have around getting work done, more of the complex
01:37tasks.
01:38And we'll continue to see progress, I think, in the, you know, more tactical tasks.
01:43But I think you're going to see more workflows and workspaces, people collaborating and some
01:48of these challenges being addressed.
01:51What are some of the biggest challenges you have faced and that you're trying to solve
01:54for at Microsoft?
01:57It's interesting, like in a way, even at Microsoft, I think AI cuts across so many different groups.
02:03So it's causing people to work together in a really different way.
02:08So our whole company, I feel like, has just collapsed into, you know, a group of people
02:13who have to take on more disciplines.
02:16So the difference between like what a product manager does and a developer does, a machine
02:22learning expert, a designer, somebody working with customers, we're just seeing a lot of
02:28things merging together.
02:29And I think that's partly because of the way AI is so general purpose and it's merging
02:35like workflows, things that were vertically for, you see things wanting to combine.
02:42So an example is we have a product that does sales, we have a product that does service
02:46and customers are saying, well, sometimes my happy customers want to use a customer
02:51service product, but then they want to buy something with the sales product.
02:55And so how can we, how can I, as a, you know, somebody looking at technologies, make sure
03:01that the systems interoperate a lot more because I don't want to have one system for this and
03:05then a whole nother system for that.
03:08Just the way companies are doing business, I think, is really being disrupted.
03:12But even the way we're developing things at Microsoft, I can feel that myself, like who
03:16should I hire?
03:17How come this team, which felt way over here before is now, you know, right here and how
03:22do we kind of move quickly and get everybody to coordinate better?
03:26Yeah.
03:27I think one of the themes that I've been hearing just from talking with people in this room
03:30earlier today is that their companies are still not sure whether they want to train
03:34the AI models from scratch, whether they want to build or buy.
03:37What advice are you giving people these days?
03:40Yeah.
03:41So I run a product which is called Copilot Studio.
03:43So that lets people create their own copilots.
03:46And what we're advising companies is maybe start with a tool that is more of a SaaS service
03:52that you can get up and running really quickly and try with your customers.
03:57And then as you need to extend that, you can go to building your own systems.
04:02One of the reasons I think we're advising companies to do that is, you know, it's hard
04:07to hire AI talent.
04:09And a lot of times by the time you build something, the system has changed and then you're kind
04:13of reworking it.
04:15So I think that would probably be my advice is, unless you have a really big AI team,
04:21I would probably start with some of the more pre-built services and then customize that
04:26as you need, because you'll get, you know, as we update things, you'll just get that
04:30for free.
04:31Yeah.
04:32Now, we talked a lot also about company culture and branding and trust.
04:37Trust being a big thing, company culture being huge, especially as you're saying, people
04:40talking about skilling, hiring, upskilling.
04:43How are you advising people on how to keep brand and company culture intact while shifting
04:48to automation, both from an employee perspective and a consumer experience?
04:54How are you building AI in a way that builds trust rather than breaks it down?
04:58I think there's kind of a couple parts to that.
05:00I mean, first of all, security.
05:03I mean, someone here, we were talking about security.
05:07Security and AI, I mean, there's so much happening, I think, on the security front.
05:13And so you definitely want to make sure that your systems are secure.
05:17That's kind of the foundation of trust, I think.
05:19And then there's, you know, just, you know, there's kind of brand honesty, which might
05:26kind of be a different variation of trust, you know?
05:31So I think that with security, even with all our focus on AI, that would definitely be
05:42our company's number one focus, just because we see so many bad actors using AI as well
05:49as good actors.
05:50So just a lot of focus there.
05:52And again, I think that's kind of why I would recommend people, you know, you don't want
05:56to build all of the systems yourself, because there's just a lot of work that goes into
05:59managing secure systems.
06:02And then as far as, you know, kind of maybe on the other side, one of the things that
06:06we see a lot of people doing is wanting to have an agent or an assistant as your front
06:11door to your company.
06:12So, you know, in the old days, you had your website, or maybe the front door of your company,
06:18which I don't think people really think of that as anymore.
06:21So you might have, you know, an assistant that's out there helping people ask questions
06:28or answer questions, and just what is the persona of that?
06:31How does it build trust?
06:33How does it keep, you know, data secure and things like that?
06:38You know, how are those experiences engaging and meaningful to people?
06:42So there's just a lot of different aspects from low level security to privacy and responsible
06:49AI to just the way your system's interacting.
06:52You mentioned bad actors, and I want to talk about that because I think that's a lot, there's
06:57a lot of scariness out there.
06:58What are some of the biggest examples of bad actors or any pitfalls you really see for
07:03relying too heavily on AI?
07:05I mean, there's probably a couple different levels.
07:11I mean, probably the thing that you hear a lot about with consumer systems is just, you
07:16know, what are you doing with my data?
07:17That's the thing that we hear the most from companies, like, are you leaking my data out
07:22to a model?
07:25Are the things that my customers say, does that go into making a model smarter?
07:29So like at Microsoft, one of the first things that we did is we took the open AI model and
07:33we definitely made it so that companies could upload their own proprietary information and
07:39their customer data and keep that separate from the public models.
07:43And that combination, I think, has been really great for people and really critical.
07:49For us, like, you know, we manage companies' email data and things like that.
07:53So obviously, if we were looking at your data or using it, like, people wouldn't use our
07:57tools.
07:58So that's just been a really foundational piece of the work that we've been focusing
08:04on.
08:05So I love that you have guardrails around that.
08:07How do you advise companies that are trying to make these guardrails and these regulations
08:13and rules within how they look at AI?
08:16How are you thinking about that at Microsoft?
08:18Well, I think what we would do is, again, it kind of depends on what level of the service
08:23you do.
08:24So if you use the SaaS services, which is kind of what I was recommending earlier, a
08:28lot of those are kind of built into the system.
08:31So, you know, what we would allow an administrator maybe to control if you were using these services
08:37inside your company, like we're definitely, you know, letting admins control that.
08:43And then as you kind of go down the stack, you can customize that more yourself.
08:48And I know this dinner is about Gen AI, and we're going to talk about that.
08:51But what is what is next?
08:53What's up and coming?
08:54What's on your radar?
08:56I think the thing that I would really love to see are more systems like the system that
09:01we built with GitHub, which is, you know, where we're really seeing real productivity
09:07and job satisfaction coming from the people that use the system.
09:11So in GitHub, we're seeing that both from the programmers using the system, as well
09:15as their managers.
09:17And like I said before, for like easy tasks that you would just want to do more repetitive
09:21tasks that you want to do more easily and more sophisticated tasks, GitHub is kind of
09:26unique because it has a lot of public data and people have kind of put a lot of their
09:32work history into a system already.
09:34So you're not starting fresh.
09:36We're starting to see that also with other verticals like customer support or for a whole
09:42company.
09:43We're starting to see that where you have all your documents, your email, your meetings
09:47and things like that.
09:48How do you how do you really well, maybe let me take a step back, like I think the advantage
09:53of Gen AI is that it can deal with conversation data, very unstructured information that you
10:00couldn't do before.
10:01So, you know, I worked on conversational ad when you kind of had to manually do a lot
10:06of this.
10:07And so it's kind of a miracle in a way that it exists and it's just been a crazy last
10:12two years.
10:13But I think a benefit for a company is taking all that information that was very unstructured
10:17either to do a sales call or to just run a meeting in your company and be able to
10:22take to get more information out of that.
10:25So people can be more productive.
10:27And so we've just been focused on that.
10:29It's it's it's it's been great to see, but we're definitely just starting.
10:33Yeah, I know.
10:34And there's so much more to do, I feel like, and as Andrew said, it's going so fast.
10:38So thank you so much, Lily.
10:39And we will leave time for questions for Lily once we sit.
10:43So thank you.