Hello and welcome to Eye on AI. In this edition….a dispatch from Davos…OpenAI ‘on track’ for device launch in 2026…Anthropic CEO on China chip sales…and is Claude Code Anthropic’s ChatGPT moment?
Hi. I’m in Davos, Switzerland, this week for the World Economic Forum. Tomorrow’s visit of U.S. President Donald Trump is dominating conversations here. But when people aren’t talking about Trump and his imposition of tariffs on European allies that oppose his attempt to wrest control of Greenland from Denmark, they are talking a lot about AI.
The promenade in this ski town turns into a tech trade show floor at WEF time, with the logos of prominent software companies and consulting firms plastered to shopfronts and signage touting various AI products. But while last year’s Davos was dominated by hype around AI agents and overwrought hand-wringing that the debut of DeepSeek’s R1 model, which happened during 2025’s WEF, could mean the capital-intensive plans of the U.S. AI companies were for naught, this year’s AI discussions seem more sober and grounded. The business leaders I’ve spoken to here at Davos are more focused than ever on how to drive business returns from their AI spending. The age of pilots and experimentation seems to be ending. So too is the era of imagining what AI can do. Many CEOs now realize that implementing AI at scale is not easy or cheap. Now there is much more attention on practical advice for using AI to drive enterprise-wide impact. (But there’s still a tinge of idealism here too as you’ll see.) Here’s a taste of some of the things I’ve heard in conversations so far:
CEOs take control of AI deployment
There’s a consensus that the bottom-up approaches—giving every employee access to ChatGPT or Microsoft Copilot, say—popular in many companies two years ago, in the initial days of the generative AI boom, are a thing of the past. Back then, CEOs assumed front line workers, closest to the business processes, would know how best to deploy AI to make them more efficient. This turned out to be wrong—or, perhaps more accurately, the gains from doing this tended to be hard to quantify and rarely added up to big changes in either the top or bottom line. Instead, top-down, CEO-led initiatives aimed at transforming core business processes are now seen as essential for deriving ROI from AI. Jim Hagemann Snabe, the chairman of Siemens and former co-CEO at SAP, told a group of fellow executives at a breakfast discussion I moderated here in Davos today that CEOs need to be “dictators” in identifying where their businesses would deploy AI and pushing those initiatives forward. Similarly, both Christina Kosmowski, the CEO of IT and business data analytics company LogicMonitor, and Bastian Nominacher, the cofounder and co-CEO of process mining software company Celonis, told me that board and CEO sponsorship was an essential component to enterprise AI success. Nominacher had a few other interesting lessons, including how, in research Celonis commissioned, establishing a center of excellence for figuring out how to optimize work processes with AI resulted in an 8x better return than for companies that failed to set up such a center. He also said that having data in the right place was essential to running process optimization successfully.
The race to become the orchestration layer for enterprise AI agents There is clearly a race on among SaaS companies to become the new interface layer for AI agents that work in companies. Carl Eschenbach, Workday’s CEO, told me that he thinks his company is well-positioned to become “the front door to work” not only because it sits on key human resources and financial data, but because the company already handled onboarding, data access and permissioning, and performance management for human workers. Now it can do the same for AI agents. But others are eyeing this prize too. Srini Tallapragada, Salesforce’s chief engineering and customer success officer, told me how his company is using “forward deployed engineers” at 120 of Salesforce’s largest customers to close the gap between customer pain points and product development, learning the best way to create agents for specific industry verticals and functions that it can then offer to Salesforce’s wider customer base. Judson Althof, Microsoft’s commercial CEO, said that his company’s Data Fabric and Agent 365 products were gaining traction among big companies that need an orchestration layer for AI agents and a unified way to access data stored in different systems and silos without having to migrate that data to a single platform. Snowflake CEO Sridhar Ramaswamy meanwhile thinks the deep expertise his company has is maintaining cloud-based data pools and controlling access to that data combined with newfound expertise in creating its own AI coding agents, make his company ideally suited to win the race to be the AI agent orchestrator. Ramaswamy told me his biggest fear is whether Snowflake can keep moving fast enough to realize this vision before OpenAI or Anthropic move down the stack—from AI agents into the data storage—potentially displacing Snowflake. A couple more insights from Davos so far: while there is still a lot of fear about AI leading to widespread job displacement, it hasn’t shown up yet in economic data. In fact, Svenja Gudell, the chief economist at recruiting site Indeed, told me that while the tech sector has seen a huge decline in jobs since 2022, that trend predates the generative AI boom and is likely due to companies “right sizing” after the massive pandemic-era hiring boom rather than AI. And while many industries are not hiring much at the moment, Gudell says global macroeconomic and geopolitical uncertainty are to blame, not AI. Finally, in a comment relevant to one of this week’s bigger AI news stories—that OpenAI is introducing ads to ChatGPT—Snabe, the Siemens chairman had an interesting answer to a question about how AI should be regulated. He said that rather than trying to regulate AI use cases—as the EU AI Act has done—governments should mandate more broadly that AI adhere to human values. And the one piece of regulation that would do more than anything to ensure this, he said, would be to ban AI business models based on advertising. Ad-based AI models will lead companies to optimize for user engagement with all of the negative consequences for mental health and democratic consensus that we’ve seen from social media, only far worse.
With that, here’s more AI news.
Jeremy Kahn [email protected] @jeremyakahn
Beatice Nolan wrote the news and sub-sections of Eye on AI.
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