Hello and welcome to Eye on AI. In this edition…China launches its own AI Action Plan…Meta hires an OpenAI veteran for new “chief scientist” role, raising questions about status of AI “godfather” LeCun…what if AI doesn’t speed up scientific progress?…and economists can’t agree on the impact AI superintelligence could have.

I spent last week in Singapore at Fortune Brainstorm AI Singapore. It was our second time hosting this event in the thriving city state, and I was eager to find out what had changed since last year. Here are some of the key thoughts and impressions I took away from the conference:

The pace of AI adoption is equally fast everywhere. With previous technological waves, many Asian companies and countries lagged the U.S., Europe, and China in adoption. But that’s not the case with AI. Instead, the pace of deployment seems equally fast—and equally ambitious—everywhere.

Everyone wants AI agents. Few are actually using them yet. Everyone anticipated AI agents last year; now they’re here from OpenAI, Google, Anthropic, and others. Yet adoption still trails the hype everywhere. Why?

Agents, by their very nature, are higher risk than most kinds of predictive AI or generative AI that simply produces content. And right now, AI agents are often not that reliable. Some of the ways to make them more reliable—such as using multiple agents, each assigned a specific task within a workflow and with some agents assigned to check the work of others—are also expensive.

As a result, Vivek Luthra, Accenture’s Asia-Pacific data and AI lead, said that most businesses are using AI to assist human workers within existing workflows. In some cases, they may be using AI as an “advisor” to provide decision support. But few are automating entire workflows.

Luthra, however, predicts this will change dramatically. By 2028, he forecasts that one-third of large companies will have deployed AI agents, and that about 15% of day-to-day workflows could be fully automated. (Accenture is a sponsor of Brainstorm AI.) This is because costs will continue to come down, models will continue to become more capable and reliable, and more companies will figure out how to redesign workflows to take advantage of these new agentic properties.

AI’s impact on the job market is not easy to discern—yet. Pei Ying Chua, LinkedIn’s APAC head economist, told the conference that despite anecdotal reports that young graduates are struggling to find work, there’s not yet much evidence of this in the data on open roles that LinkedIn tracks. That said, there has been an uptick in the average number of applications required before coders land a job.

On the same panel with Chua, both Madhu Kurup, vice president of engineering at Indeed, and Sun Sun Lim, vice president at Singapore Management University, emphasized the need for employees to acquire both AI skills—techniques for prompting models, familiarity with how to build an AI agent, an understanding and of the strengths and weaknesses of different kinds of AI—as well as human “soft skills.” As AI transforms all jobs, soft skills like flexibility, resilience, and critical thinking matter more than ever, the two panelists said.

Jess O’Reilly, Workday’s general manager for ASEAN, said that she thinks AI will lead many companies to adopt an organizational structure based more around teams from diverse functional areas coming together for specific projects and then being reconfigured for the next project. She said this would be like an “internal gig economy” for employees. Traditional reporting lines and vertical organization would need to change in favor of a flatter, more dynamic org chart, she said.

Infrastructure is destiny. From several panels at Brainstorm AI Singapore, it was clear that access to AI infrastructure is going to be critical. This is true even when countries don’t want to build their own models. Just running models—what’s known as “inference”—also requires a lot of AI chips.

But building data center capacity requires big investments in energy. Rangu Salgame, CEO and co-founder of Princeton Digital Group, said that in the near-term fossil fuels, especially natural gas, would likely be used to power the data center buildout in Asia—which is not great news for climate policy. But in the medium term, he saw great potential for AI data centers to force countries to build out renewable energy capacity, such as solar power and off-shore wind.

Sovereign AI matters. Delivering it is challenging. Everyone is talking about the need for sovereign AI—and that was certainly the case in Southeast Asia, too. Governments want the ability to control their own destiny when it comes to AI technology and not become overly dependent on solutions from the U.S. and China. But achieving that independence is tricky, as was clear from several of the sessions at Brainstorm AI.

While increasingly capable open source models are giving governments some options in terms of which models they choose to build their solutions on, there are still some big constraints.

First, there’s the huge cost of building out data center capacity and constructing the power plants and upgrading national grids to support it, which I mentioned above. Then there is the issue of training AI models that are adept at local languages and also understand cultural nuance. This requires curating data sets specific to local context, said Kasima Tharnpipitchai, head of AI strategy at SCB 10X, which is building an LLM for the Thai language. “There are no tricks here, you really have to do the work,” he said. “It really is just effort. It’s almost brute force.”

Embodied AI is China’s big strength. While it often looks like the U.S. and China are evenly matched when it comes to the capabilities of AI models, China has a massive advantage when it comes to “embodied AI”—that is, AI that will live in physical devices, from robotaxis to humanoid robots. That was the message from Rui Ma, founder of Tech Buzz China, who spoke on a fascinating panel looking at the geopolitics of AI. China controls almost the entire robotics supply chain and is making rapid progress creating cheap and practical robots designed for factories, as well as general purpose humanoid robots. (One of those humanoid robots—Terri, which uses software from Hong Kong startup Auki Labs, but whose body comes from Chinese robotics company Unitree—wowed delegates at Brainstorm AI.)

There is a middle path between the U.S. and China. Singapore has consistently tried to thread a path between the two superpowers. And at Brainstorm AI the country’s digital minister Josephine Teo said that the country was finding places to act as a bridge between the U.S. and China. For instance, in late April, Singapore played a key role in hosting a meeting of AI safety researchers from both the U.S., China, and elsewhere that arrived at what is called the “Singapore Consensus”—an agreement that AI systems should be reliable, secure, and aligned with human values, as well as a shared vision about ways to ensure that is the case.