LIVE: Microsoft CEO Satya Nadella interacts at World Economic Forum session in Davos, Switzerland

It took a long time for even the smartphone to penetrate all of the world, whereas now it's not the case. These models and their outputs are pretty much available everywhere. And so the question to me is what are the use cases that make sense? One of the demos I always go back to, even in the beginning of 2023, was a rural Indian farmer who was able to use a bot built on a very early GPT-3 or even GPT-3.5 to reason over some farm subsidies he had heard about in a local language, and it even showed some agentic behavior, like go complete a form for me. In some sense, it brought back agency to someone who perhaps didn't have that because the technology was so much more accessible. So I do think it's in our hands, even in the global south, to use it to create more opportunity where there isn't one. But the necessary conditions still are: do you have the capital investment being put in? Do you even have an environment for capital? Because in an interesting way, we as hyperscalers are investing all over, including the global south. So as long as there's an environment that attracts the capital investment, and you see the demand, then the question is how do you have a set of policies that allow for both the capital to come in and find nexus? There are certain things that private capital can do, and certain things that only public capital can do, like the grid. The grid in most countries is fundamentally driven by governments and public. So if you don't have a real approach to modernizing the grid, that will hold things back. There's a lot of talk about behind-the-meter and so on, and yes, there's some amount of that we can do ourselves. We can do that in the US, but many countries can't. And it's not long-term scalable. To me, a long-term scalable solution is to have all of these token factories as part of the real economy, connected to the grid, connected to the telco network, delivering just like we delivered bits, you have to deliver tokens plus bits. And that's what's going to drive scale, whether in the global south or in the developed world.

For this not to be a bubble, by definition it requires that the benefits are much more evenly spread. A telltale sign of a bubble would be if all we're talking about are the tech firms, if all we talk about is what's happening on the technology side, then it's purely supply side. Ultimately, if we are not talking about a drug that was brought into the market and was super successful because it was AI-accelerated in the clinical trial, it's not even the magical molecule, it's the rest of what's needed to make something much more relevant. And by the way, it's happening. So I'm much more confident that this is a technology that will build on the rails of cloud and mobile, diffuse faster, and bend the productivity curve, bringing local surplus and economic growth all around the world, not just economic growth driven by capital expenses. That's a narrow point-in-time calculation. That's what we're seeing in the developed world in particular, but remember, we're spending a lot of capital in the United States, but 50% of it is also all over the world. So interestingly, it depends on demand all over the world, and that demand will only be there if there is local surplus. That's the way I see the equation.

I think it's one of the big challenges with all these new technologies: when work artifacts and workflows change, the means we as firms have to change how we work. I remember meeting the CEO of General Electric a few years back, and he was describing how they worked with their agents in the field with faxes and interoffice memos. Then suddenly the PC showed up, and people would put a spreadsheet in an email and send it around, and the entire workflow changed. Similarly, with AI, you're going to start seeing actual change in how workflow happens. For me coming to Davos, preparing for 50 bilateral meetings had a particular workflow: my field team would prepare notes, that would come to my HQ, and get further refined. Nothing had really changed since I joined in 1992. Now I just go to Copilot and say, 'Hey, I'm meeting Larry, please give me a brief,' and it comes back and gives me a 360-degree view. It knows what we're doing with you as a client, what we're doing as a client of yours, and everything in between. It captures information unlike anything else. I take that and immediately share it with all my colleagues across all functions. It's a complete inversion of how information flows in the organization. It's not like the classic model where we have departments and specializations and information trickles up. No, it flattens the entire information flow. So once you have that, you have to redesign structurally. The current structure may not make sense because you want people to work in a way that allows information to flow freely. The formula starts with mindset. As leaders, we need to think about changing the workflow with the technology. Then you need the skill set. You can't talk about this in the abstract; you have to use it. If I'm not using it, I can't trust it. You have to learn how to put the guardrails in place to trust it. You can't just be afraid of it; it's going to be diffused. So as a firm, you have to use it to learn how to put the guardrails that allow you to trust it. So mindset, skills, and the other big consideration is how you make sure you have the data set that you're feeding, like context. It's like you have a new intelligence layer, but it's only as good as the context you give it. People describe it as context engineering. That's what firms do: it's all about the tacit knowledge we have by working in various departments and moving paper and information. The question is how do you have this AI also have that context? These are some of the new things that have to percolate throughout an organization to take advantage. That's why you're going to see the challenge of 'Why am I not seeing immediate results in productivity?' Because you have to do the hard work. It's not going to be some firm-wide difference; it's going to be fundamentally because of the leadership will in an organization.

It is a barbell. Small companies that are just starting use that platform 100%. Even for large organizations, there's a fundamental challenge because unless your rate of change keeps up with what's possible, you're going to get schooled by someone small being able to achieve scale because of these tools. Large organizations have inherent strengths: relationships, data, knowhow. But if you don't translate that with a new production function, you'll be stuck. So the change management challenge for large organizations is bigger, and the structural challenge for small organizations of overcoming scale issues is harder. It's going to be a very competitively intense world where neither side can just coast.