Exploring the future of business, this session examines how companies can become more agile and intelligent by embedding technology and data at their core, creating organizations that can sense, adapt, and thrive in a rapidly changing world.
At Davos, panelists argued that becoming “AI first” is less about piloting chatbots and more about redesigning core work. Rubrik CEO Bipul Sinha defined maturity as embedding AI into “3 to 5 workflows…across all of your line of business,” replacing manual, end-to-end processes rather than adding a thin interface layer. You.com’s Richard Socher emphasized the human operating model: adoption rose only after training because “every one of us is going to have to become a manager…managing their AI agents.” Lightspeed’s Ravi Mhatre urged leaders to think from first principles—“if we had access to unlimited intelligence, what would we build?”—but noted enterprise progress is slowed by compliance and risk mindsets. Trust, governance, and benchmarking emerged as gating factors; Sinha described translating policies into “a judgment model” so “LLM can judge LLM,” while Socher warned most firms lack “a principled, more scientific process” for evals. Moonshot AI’s Yutong Zhang highlighted “bring your own AI to work” and the rising leverage of lean firms where “less than ten” people can run operations with “hundreds of agents.” The group predicted near-term shifts toward “software becoming invisible,” agentic orchestration layers, multi-agent dynamics, and longer-horizon autonomy—“AI will build the best AI, not people.”
Hi everyone. Both in the room here in Davos. And joining us online I'm Ina Fried chief technology correspondent at Axios here to talk about the topic that I think we're all talking about these days in terms of AI and particularly here in Davos, the the topic of how businesses can best harness AI where businesses are getting stuck has been the topic of conversation, both on stages as well as along the promenade in shuttles. We're all trying to figure it out, so I'm very glad to be joined by an incredibly esteemed group of panelists. And in this session, we're going to dive into another of the tensions, which is the difference between large companies, existing companies that are trying to adapt to AI, and the new companies that are being born AI first. And can older companies become AI first companies? So I'm delighted to be joined by this amazing panel. Just throwing out a few stats. Private sector investment in AI was more than 250,000,000,000 in 2024. We'll still have to wait and see. These guys have been writing some more checks and cashing some more checks, but only 1% of large enterprises considered themselves truly mature in AI. And for the purposes of kind of all being on the same page, I'm going to throw out this definition that was given to me that we could consider a business AI first, if over half its workforce tasks realize at least a 20% performance gain. I think we might have to use some other metrics than that in our discussion, but I'm going to throw that out, and I want to point out the World Economic Forum's AI Global Alliance launched a new Workstream with A.T. Kearney looking at this, covering more than 50 innovation leaders. And I'm sure you can find out a bunch more about that online. So I'm going to introduce the panelists, not necessarily the order they're seated, but, we have Yutong Yang from who's the president of moonshot AI. We have Janice Antognoli, who's the co-founder and president of reflection AI. Ravi Mootrey, who is the founder and partner of Lightspeed Venture Partners. People. Sina, who is the co-founder and CEO of Rubrik. And Richard Soker, the co-founder and CEO of Ucom. So we have a great group. Again, I value that. We have so many different perspectives. I'm wondering for each of you, I mean, I threw out one definition of what makes a company AI first, but you all are seeing it in the real world. Can you talk about any companies that you would think of as AI first that weren't born after ChatGPT? Any companies that you would say still meet the definition of AI first, because of how they've transformed themselves?
I can go first. In our mind, if you say that you are an AI company, you have to really define do you have like, end to end business workflows that is completely being done by AI as opposed to just doing information search or using ChatGPT interface? So what we are doing in Rubrik is we are going line of business by line of business, and asking everybody to identify 3 to 5 workflows that are done manually, or combination of SaaS application and human beings. And can we define end to end AI driven outcome? And so my definition is that if you have 3 to 5 workflows that are AI implemented across all of your line of business, then you are legitimately an AI driven company.
Anyone else want to throw out? Maybe either a different definition or perspective? Richard.
I think you have to think about both sides, the technology, how many agents do you have, and so on, but also the human side. How many of your people are actually certified and know like one thing we found in You.com is when we just said, oh, here, use it and create your own agents, you can delegate all your work. People are like, okay, but I don't really know how to delegate work. Most individual contributors aren't managers. And then the adoption would be very low until we said, well, here's a training program and a certification program. And then people actually, when they had to do it, then you saw adoption of these older organizations really pick up and start to embrace it, because every one of us is going to have to become a manager, whether they're managing people or managing their AI agents. But that managing and delegation mindset doesn't come naturally to most people.
And Ravi, you see a ton of companies. Do you see any again that weren't born in the last year or two that you would say have really latched on to AI in a way that would lend you to label them AI first?
Well, I would say that our it's interesting. The the World Economic Forum's definition is a little bit of an averaging effect of some percentage improvement across the entire organizational set of capabilities. And I think the reality is that is that's probably not unlocking the full potential of AI. And I think again, we see it with AI native companies. Anthropic as a company were closely involved with, if you look at their ability to do things that are transformative with AI, I think the imaginative mindset of AI first companies is they start with, if we had access to unlimited intelligence, what would we build? And they're obviously not held back by having organizations and preexisting workflows. I would say with traditional companies that have to cross over to try to be really, AI first. The challenge is they are mostly looking at what do we do with the technology as it exists today. And they are also balancing that with the fact that their organizations have a way that they work. And I think the few that really will break through the today, we don't have totally unlimited access to intelligence, but AI continues to improve. Some of the people are really do expect it will continue to improve exponentially in terms of the capabilities of AI. And so traditional companies need to be thinking about the same way. More Transformatively. If I had unlimited access to intelligence, how would I make my business go in a way that really I don't have any preconceived notions of how I do it today. And the the trick is, if you applied that framework, it's a very uneven surface area encoding you really with cloud code, which only came out eight months ago. It's a 1.5 billion run rate revenue product already for anthropic has almost 15 million developers. But you would have some problems where truly you could have the AI do the vast majority of the work and be ready to ship a production piece of code that was unheard of even 24 months ago. So you you might have real success there. You probably wouldn't have AI, you know, doing some other form of highly regulated part of your business, like trying to create financial statements. But the thing is, is the companies that really develop that mindset, and I think it comes from companies who have really vision oriented management CEOs or founders driving the businesses. It's very hard for an organization to make that kind of change, and you get results that are uneven. But the companies that are making that kind of mindset shift now, I think are the ones that will have the chance to really survive and thrive in an AI native world.
Sounds good. And you talking. It would be super unfair to ask you to represent an entire country, especially one as large as China, but help us understand how the how the interplay is going between these startups that have been born since, the AI models and some of the older companies in China, some of the tech giants in China. What are you seeing?
Yeah, I think, also like for, organizations, I think, one approach is I think, like Ravi said, is like more top down. The management have the AI native mindset. But the other perspective we see interesting is bring your own AI to work. So we actually have a consumer app where we have tens of millions of users from the globe. We actually observed that, you know, we have the user research and interview with them. And a lot of people that are using AI at work, and they are willing to pay out of their own pocket. So I think which means AI can really enhance the productivity in a lot of the jobs that we are doing today. I think that would be very interesting. But from the organization level, I think, a couple of things may be interesting to see. I think one is the human to agent ratio. You know, the company, ran out all the startup company are really, really lean and small. We have like 300 people, but we are like building models, building applications. And we saw some company, if they are purely just building an AI applications, they have people like less than ten, but they have hundreds of agents helping them to run a lot of things on an operational level. So I think right now, AI giving all the companies a really high operation leverage.
Yeah, I mean, I do think that that's one of the discussions that I've heard start in Davos that I think is going to really continue throughout the year, is this competition is no longer you and your existing competitors. If you're an existing startup or existing company, it's really you against somebody who started from scratch with no workflow, with no process, with no legacy that says, well, I can do that. And obviously the incumbents don't come to this with no assets. I don't think that. But, can you talk and whoever wants to jump in first? But I'm curious, you know, obviously every company of more than one person has a workflow among people. How do you make that an asset? How do you use AI within the organization while still recognizing that it's humans that have to absorb the chain? And I'm curious if anyone maybe has examples from their own company of how you're harnessing AI, while recognizing the fact I don't think anyone's company here fits in the category of more agents than people, or at least not a lack of people.
The difficulty of like AI adoption is. We heard about bring your own AI to work, but the particularly American company is the compliance. The governance actually restricts how much data you can feed to AI that you want to bring to work. So what we have done is we have really created a kind of like a compliance infrastructure, saying, here are the set of data, and here are the set of certified models that you are allowed to interact with. And then based on that, we are actually applying coding, legal, marketing, customer support. So every function is working within that category. The biggest difficulty overall we see in our customer base is they have a lot of pilots and they love the outcome of those pilots, but go from pilot to production. Their biggest worry is compliance risk and then chief risk officer CISOs are coming down and saying, how do I ensure that these agents are within their guardrails? Are they hallucinating? Are they compromised by nation state threat actors?
And I think, won't that be where some of the tension is between the older companies? That may be slowed by their legacy, but already understand those things. And the one person startup that vibe coded this amazing demo, but suddenly wants to go into an industry where they have to meet compliance. So the flip side is, you know, what are some of the challenges for these AI native companies that might be, you know, eight people and 800 agents, but have to follow a set of rules and laws?
I mean, we definitely have more agents than people inside. EW.com I think one of the biggest challenges that we see for, companies that want to embrace AI is that they often don't have yet a benchmark or evil set mindset. And this is, I think, a big divide. If your company can identify what are the inputs and what do you classify as a correct output, and you create a set. And it's not just like, oh, I tried these three things and it worked. Or I tried these four things and it didn't work. So I don't want to use that tool. You got to make it into a principled, more scientific process. And if you have that benchmark, we love working with those folks because then we win, because we have the best models and answers and accuracy and citations and all of that. But the vast majority of companies out there have not yet thought about benchmarking and creating sort of scientific ways to evaluate their AI models and their agents.
And Janus, you obviously, you know, are an AI native company in the sense that you were born of and for this mission, but also have been around long enough that I'm guessing there's a few people involved as well. Talk about how you view both for your own company, but also for your customer base. This idea of how you know it's going to be a mix of legacy companies that have humans, and some of these new startups that started with very few people.
Maybe, maybe I'll just start by saying, you know, going back to the previous conversation of like, what's an AI first in my mind? And AI first is the idea of just like seeing what are the workflows that you have and what are the things that you've done. And then just like, try to rethink them from like first principles, knowing that AI exists and knowing that AI will just become better. And of course, you know, the new workflow will have like humans in the loop. It's not going to be just AI, and you need to just be really careful on how you design and architect the system so that you can maximally use your people and ensure that, like you don't used like human labor is actually used where it's necessary and is used where it's like the most efficient. So I don't see a world, at least for like the next few years where, or actually the foreseeable future where, like, humans will be completely out of the picture. I think that, like, there's going to be a combination of humans and AI's just working closely together to just increase productivity and ensure that, like, everything falls into place. So like now in terms of, I think like what's happening with traditional industries is that they have a lot of the know how on like the, the things that they need to be solving and the problems that they need to be solving, especially like with respect to compliance with respect to their customers and the industry, but they don't really fully understand AI. And this is where it's important to actually like work with other companies like AI, companies that have the deep understanding of how AI works and what it's capable of to kind of have this synergy of like bringing the industry experience and bringing the AI experience, working together to rethink and re-architect the workflows so that they're optimized for the AI age.
And Ravi, you're obviously investing in companies. I'm curious to like how you look at like, what what do you want to hear from a from a startup CEO in terms of how much are they relying on people versus AI? Do you want.
To hear just.
Go back to one thing that I mean, I really be curious as to John's view, but people alluded to it that today the business leaders for the large enterprises, these are not AI first companies. They do even if they are trying to reimagine what access to unlimited intelligence could do to reinvent their businesses. This need for trust and safety. It's turned into a compliance mindset, which actually slows down the adoption and the usage of AI in their businesses. And what we need is really, I believe it's a set of both technologies and then a mindset that allows for AI to be safe and trusted, because trust, if there were trust based technologies embedded in AI, then I think these more traditional enterprises and business leaders could be in a lean in mode where they say, you know, as I scale this, I know that safety will be resident in it and so I can go fast as opposed to thinking about, as I introduce this new technology, how do I, you know, go slow or why should I be more conservative? And I'm just curious. I mean, I think, you know, you alluded to the fact that right now it's evals and business leaders and traditional enterprises, they don't have the wherewithal or the expertise in their companies to come up with sophisticated evals. And so we think that one of the critical accelerants for adoption, at least in democratic AI, you know, based societies, has got to be productization of trust based technologies somewhere inside or alongside of the models. And I'm just is actually building, you know, frontier level reasoning models, that are open source, the companies based in the US. But how do you think about the trust based technologies, again, as those kinds of systems are diffused into into the world and into businesses, you know, that would allow them to be used, you know, kind of in a good way.
Yeah. That's a really good question, actually. Like, because this is, I guess, like one of the main obstacles in the adoption of AI, like, how do you build the trust and how do you ensure that, you know, like what the AI, what you think the AI is doing is actually what's doing and, you know, actually like it boils down to having better technology. Like when GPT came out in 2022, I think that like, there were all these hallucinations and everyone was like really skeptical of anything that they would just read on GPT and they would just like double check it. And, you know, in most cases it was actually wrong. But like now from from most for the most part, when you just read something on your ChatGPT, you trust it, you're like, okay, you know, probably it's right. You know, the chance is because you've actually, double checked it a few times, and over time it actually like, it won your, your trust. You actually like, saw it being correct time and again. And over time you actually develop this trust with AI. And I think that like, AI is not it's something that, you know, we will be interacting a lot in different ways in the near future. And we will just like start developing this trust with AI systems in general. We'll just like start to trust them all the same way that, you know, even when computers came out, people didn't trust them. But like now, everyone trusts the computer. They'll just like, do the things that you told them to do.
So I was just going to ask you, is that something that is is it approached differently in China, or is it a fairly similar notion of trust and the need for trust in order for businesses to use AI?
Yeah. I think actually trust can be enabled by technology. And trust is also a perception of the users. Right. It's like when we first launch the application, we are connected with, search tools. And also, you know, we show all the citations, all the references where I got the information from the web or from the files to the specific sentence, you know, this type of layer of interaction actually help people to feel, more trusted, toward the technology. And also, I think chain of thought is another thing, that AI was like a black box before, you know, they give you a number from a complex calculation. You don't know where the number is coming from. But right now, with like all the reasoning steps, we can see, like, the whole trajectory of how AI, you know, using tools, gathering information together to synthetic the the answer. So I think, definitely, that is very important. AI cannot just, you know, providing the direct answer. It has to just unbox all the thinking process, all the tools, all the data sources. So I think there are a lot of infrastructure to be built there to make people feel okay. They can trust the technology with, not only execution and also on, a lot of decisions on how to run the business or, you know, they can really let the IOM to decide a lot of the things if people can, define what what good is looks like, you know, having all the expert opinion, being the rubrics. And then we can just really building a scalable, decision system driven by IOM.
And people. I'm curious like how your because I and others may have thoughts on this and feel free to jump in after people, but you know, you're building a business to help other companies, you know, build for this AI world. And I'm curious today and I imagine this will shift. How much are you thinking of helping the larger established businesses, and how much of your thought is shifting to how do I make sure I'm a good partner for that generation of couple people startups? Where is the energy today and how much are you thinking about the latter?
For larger corporation, the question is what we were talking about is the trust, and this is what we are focused on. We are helping them adopt agentic work. What we have understood is that many of these organizations have documents which say, this is what you are allowed to do, this is what you're not allowed to do, but it is very hard for them to translate that into a set of rules that your agents and your models can perform, because those are probabilistic systems. And this is a fixed set of rules. So what what we are working with our customers is can we take this set of rules, train a model that will be judgment model on your agentic work. So you are now judging an LLM with another LLM that is based on your own set of rules, similar to what you were describing. As a result, you don't you don't have to create rules. Let the neural drive the rules so LLM can judge LLM. And then you have better confidence in terms of the accuracy of these results. So these are the directions that our customers want to go. They don't want to be in the business of translating their business rules into stopping agents. They want to enable agents, but they want to enable agents in a way that they have higher confidence in hallucination rates, third party cyber attacks, surface area is bigger. So those are the issues that we are solving for.
So it sounds like you still see your business as helping larger companies adopt AI. And I'm curious maybe, Richard, do you have thoughts on how that needs to shift over time so that even upstarts are thinking, how do I help these new generation? Because I imagine, you know, when we're sitting here a year or two from now, somebody on this panel is going to be running $1 billion or more valuation. Although now that seems like nothing these days. That's a pre-seed startup. But, you know, some some substantial valuation of company with only a few people. And those will be some of your customers. Richard, how do you think about that?
I mean, in general, of course, everyone loves scale and large customers can bring scale, but large customers also have just much, much longer procurement processes. Right? And working through those is hard as a small startup. So it's a trade off. The more smaller startups you have, the faster you can move. And you can hope that some of those startups actually will scale and keep growing. And then when it comes to trust is something that is really important for us to actually in 2022, we filed patents and shipped the first connection of a search engine with an LLM so that you actually have citations. And I think citations have now obviously been, like everywhere. And they're a way to build that trust over time. And that's again, coming back to that delegation capability. Part of delegating, well, is knowing when to trust and when to trust but verify and then kind of build that over time. But yeah, overall, my hunch is the biggest companies in 20 years are those that are starting right now and are AI native.
And Yutong. You mentioned early on that some of your customers are actually individuals bringing your chatbot in to help them do their jobs better, whether their employer is AI first or not. How do you think about who the customer is today for your chatbots, but also who the customer of the future is?
Yeah. So I think, a very important, change is, I'm not saying that software will disappear, but I think software will become invisible. So I think, right now is like, you know, if we want to create a word document or we need to remember all the Excel formulas to using Excel or create a very beautiful, presentable PowerPoint slides, people are directly, you know, using the GUI, you know, clicking the hundreds of buttons. Remember all the formulas. But I think in the future, definitely, human will just using natural language to access all the powers from all the tools and all the software's, through agent. I think AI can help us to use, get access to, all the software's. I think that's the way that it will change, you know, how we work in a lot of the companies where people have access to AI as a superpower, to help with their work.
Yeah. Somebody said this week, and I apologize. I'm stealing someone else's wisdom. But, you know, there was the famous Andreessen saying of, you know, software is eating the world and that AI is eating software. Is that your sense? I don't know.
I just think that, it's more like a UI and UX, you know, transformations. Because we are relying on, you know, a lot of the clicks and the keyboards for the previous interactions. But I think the interaction going forward, will be more natural that people, as long as they, you know, they can describe their intent, they can access, you know, they know how to use AI, and the AI can help to help them to access all the powers that offer by existing softwares. And if there is no existing softwares, AI can use their coding capabilities to write a personalized tools or, you know, on the spot to help to deliver, the end results. I think that's definitely.
And I would agree 100% with you. I mean, it partly is related to the fact that people don't appreciate that it really is an exponential curve in terms of the improvement in, in AI or intelligence kind of capabilities. In the last year, the on average, the cost per token for inference on average went down 100 x in many cases of use cases went down 1000 x. So when that's happening, just this concept of intelligence being able to essentially make software disposable, where it can be coded with robustness as in the Clyde Coast case, and it then can be mostly production ready. The ability for just, you know, someone to imagine any task that you want to automate via a piece of software and use intelligence to make that happen. If we're on this exponential curve of how powerfully intelligence can sort of show up for a given cost parameter, I think you will see that that software becomes invisible and intelligence will be sort of the new kind of language in which people express what they want, you know, automation behind the scenes to do. And it will just be kind of a continuous flow of that.
If we take that as the assumption. Does anyone want to challenge the assumption that that's where we're headed? But if.
We.
Take my hunch is there will be some software where you really want it to be just really well done. And it's a very common use case like Instagram or something, right? You could hack up an Instagram, but then you don't have the.
Network scale.
You don't have, you don't have the people contributing their content and stuff. But I agree that like, especially in enterprise, if you do it cleverly and you have all the complex kind of permission sets and reporting and so on, if you can automate that and then innovate on top of that, then it would be very powerful.
Yeah, I think this will expand the use cases, just places where software doesn't get built and you have more manual processes and workflows that will likely go away. And again, the rate of change, because it's more of an exponential improvement in the capabilities of intelligence, I think will surprise people. But I agree with you also that applications where you have many, many users and so fit and finish and certain things about robustness of the design and capability matter, you know, that will for a longer period of time, maybe ultimately in the limit that also can be automated. It will be some combination of engineering people who who use AI to create leverage in their workflows, but also have to do some of the design.
So if we take.
That as the assumption of where we're headed, what are we not considering in terms of how that comes? For example, I think one of the things we underestimated with AI is just how fast humans would be able to adapt it. If we assume as a starting point that we are going to be in this world where businesses are going to create these layers that just do what needs to be done without creating another piece of software, what what might we be missing? What do we also have to pay attention to before we run full speed ahead? What are we going to what still needs to be done.
In my mind, genetic orchestration is an area that is going to be very important because if you think about all the software that we have built in the last 15, 20 years to automate the enterprise for digital transformation, you this new Agentic orchestration layer will take make use of those software is just that. Human beings will not be involved in the process, and over time, it will go deeper and deeper and deeper and make the workflow engine totally useless. So ultimately you will have the data structure and then you have agentic orchestration. So what, is less understood or not yet been focused on is this Agentic orchestration layer that takes input from you as what do I want to accomplish? And then how do you create and orchestrate the agents? So in my mind, if you fast forward 1015 years, you will have data, an agent orchestrator that can create any kinds of workflow that you need, whether you want to take a business order from a customer, or you want to run a campaign to your customer or prospects, or you want to support your customer, it will be dynamically generate that workflow for you. Run that workflow, create the result, give you the outcome, and say, this is what has happened. But that's a process. That is an orchestration process.
I would I would say maybe there are two things. One is right now AI is, we're moving away from more and more from infrastructure that was initially built for humans. And now AI is kind of starting to use it more and more. We've seen this like GPUs initially for people playing games, and we train them. Now we're building new hardware that is only made for AI. We see this as you move up the stack, like a lot of AI models used to use Google as their search engine, slurp API's and so on. Now they're actually transitioning and we're building an actual search engine just for llms for them to search for us and then summarize everything. I think the other thing that we're massively missing and not considering is what the next level might be. And the way I can imagine that from history is, imagine when the IBM Watson team just won jeopardy! Everyone's high fiving. You're like, good job. To the team that worked on geography questions. Good job to the team that worked on music questions and celebrity questions and so on. But by the way, the 800 of you are going to be one neural network because we can learn all of that, now and replace a manual system with a learned system. Right now, the system of building models is actually done manually. Really smart people that sometimes you have to pay $1,500 million to are manually creating intuitions on what works when they create new models, and then it takes them months to merge a new model. And then you have GPT 5.3 or something. That part itself can also be automated, and that will be sort of recursive. Self-improving
Models sort of get better on their own.
Exactly.
And how about from you? What what do you think some of the things that aren't problems today? Because we aren't AI native, but when we get AI native things that become problems or challenges.
Yeah, actually, I'll just go back to, the orchestration. And I think it's not just like a problem of agent orchestration, it's rather the problem of multi-agent. And, when you have a system that, you know, it's, both humans and many agents interact with each other and what the dynamics of these systems are, it's not a well understood system. We don't really have, like, any science, really, behind that. We don't know how to optimize these models to operate better in multi-agent systems. You know, there is like some early research on that, but, like, it's not mature enough. And I think that, like this, there's a proliferation of, like, agents and proliferation of, like systems with like many, many agents interacting with each other. This will become more of a pressing problem.
So for our last question for each of you, what's something that sounds kind of radical today but you think will be obvious by the end of the year? By the time we get to Davos next year? Sounds like a great prediction today, but you think it'll be common sense wisdom by the time we get to rejoin next year.
That AI will build the best AI, not people.
I think it's a really long agency. I think before that we really have a usable, scalable AI system. The AI need to have really long agency, like human, you know, like, you know, I was working with my team is like, we have a weekly meeting, and we set up the goal and we discuss, you know, roughly about the task, and then they just go off and they can autonomously, working on their own and everything. And then we can meet at the next week and to see the results. So I think the really long agency will probably happen.
Any other thoughts?
I think we'll see this start to see the first signs is about, you know, the kind of frontier of, of kind of intelligence capability, the, the ability for some form of more continuous learning. It may not be perfect, but the ability for models to be adaptive for how they interact with their environment and then kind of update their, their core knowledge, said people.
For for businesses, RL is not properly understood today. The challenge with a lot of businesses are there are no like generic employees. Everybody does a specific task for the business and that is the business processes that is defined for that business. And what is not well understood is how is RL going to take that specific workflow for the business and do reinforced learning to make a generic model work for business, and that we'll see next year as the as the big thing today is like little theoretical, but that's what will accelerate the adoption of AI in the enterprise.
Janice, you get the last word.
Yeah. I mean, I think I agree with you that, really long agency is going to be like, commonplace.
Well, I hope we get the chance to discuss all that next year. I'm sure it will be a year of great change. Thank you so much, Janice. People, Richard Yutong and Ravi for an incredible discussion. If you're enjoying this, I do a daily newsletter for Axios. You can go to Axios newsletters and you'll be hearing from folks like this in your inbox every day. Thank you.
Thank you. Okay.