Walking around Dubai, it’s hard to believe a war is still going on in neighboring countries.

No missiles have struck the city, and it was clear during a recent visit that local residents are keeping to their routines. The biggest public worry, as it has been in many parts of the world, is the extreme heat. (The high today is around 106 degrees.)

Dubai, one of seven emirates in the United Arab Emirates (UAE), continues to emerge as a major global tech hub and is on the verge of becoming a major AI powerhouse. Each of the emirates has their own laws and traditions, with Dubai and Abu Dhabi — the UAE capitol — the most prominent.

Other nations recognize the region’s potential and Dubai’s position. The US, for instance, showed interest in currying favor with the UAE by sending a CIA spy to vet the emirate’s top AI firm, paving the way for future chip and AI deals. That outreach, highlighted in a report in The Wall Street Journal, was a hot topic in the local tech community.

These days, Dubai and Abu Dhabi hope to position themselves as hubs where people and companies go for AI. It’s location makes it an excellent place to do business with connections to Asia, Europe and Africa.

Broadly speaking, the UAE wants to be something of a Switzerland for AI, or “a neutral AI corridor between East and West,” Mahmood Abdulla, a regional technology expert, explained in a 2025 LinkedIn entry.

Data center buildouts

While states like New York are putting a moratorium on the construction of data centers in the US, the UAE is moving in the opposite direction; an aggressive AI data center buildout has government backing.

Stargate UAE, a data center project that involves top tech firms such as OpenAI and Oracle, is being built in Abu Dhabi. The city has essentially planned out a business model designed to turn AI into hard cash with a focus on the energy resources that will play a major role in the effort.

The plan is to redirect Abu Dhabi’s vast energy resources — it has up to 100 billion barrels of known oil reserves — to data centers generating AI tokens. Those tokens can then be sold as compute resources to produce revenue.

With oil markets volatile because of ongoing conflicts and the worldwide rise of renewables, pushing energy use to AI data centers might be a more reliable form of revenue long term.

Will it work? That remains to be seen. But unlike the US backlash against data centers construction in many place, a ban here seems unlikely.

The entrepreneurial spirit

Technological innovations from entrepreneurs have been at the core of Dubai’s rapid growth in recent years as a global business center. Here, AI is already quietly woven into daily life, much as it is in China, where AI is already in factories, homes, and infrastructure.

One founder told me that naming his company to include “AI” is more of a US thing. UAE companies focus less on boasting about AI models and superintelligence; they tend to emphasize user experiences.

On a separate track, Dubai is already targeting the launch later this year of robotic air taxis flying from the airport into the city. (That’s ahead of New York City, where electric air taxis are still being tested.)

Still, Dubai is behind the US when it comes to autonomous ground transport. The first autonomous taxis hit Dubai streets just last year, and there are a limited number operating in tourist areas.

A complicating face tor for the area remains the Iran war. Dubai’s Internet City, a hub with buildings belonging to the likes of Microsoft, Oracle and IBM, largely emptied when the war started in late February. Foreign tech workers left, and property prices declined.

But the real estate market is booming as opportunistic investors buy low. Their bet: the US-Iran war won’t stop the UAE’s growth.

Money is flowing into the area in other ways. Heavy tax burdens in Western countries are pushing wealthy founders to move businesses to Dubai’s tax-free environments. And Dubai is opening the door to those with digital nomad and freelance visas. Although visas can cost thousands of dollars a year, lower rent and healthcare costs compared to the US can offset those costs.

Rivals are emerging, too

UAE’s economic rival, Saudi Arabia is also rising as a tech powerhouse. Riyadh has undertaken reforms at a rapid pace to become an attractive place to do business under its Vision 2030 program.

Saudi Arabia requires corporations to have regional headquarters in the country to win government contracts, a policy that challenges Dubai’s status as a regional business hub.

The UAE also relies heavily on immigrants and human labor, which can be both a plus as well as a challenge. There is no pathway for entrepreneurs or long-time tech workers to retire here, which can undermine worker motivations to stay.

Still, the attractive business environment, combined with local infrastructure and a skilled labor force, make this area appealing. As one founder told me, if the US doesn’t want its most skilled immigrants, the UAE will be happy to welcome them with open arms.

Anthropic CEO Dario Amodei has argued that policymakers should keep lower-risk open-weight AI accessible while placing stricter safeguards around frontier systems, including mandatory testing and limits on China’s access to advanced computing and model capabilities.

In a post outlining Anthropic’s position, Amodei said broad restrictions, including bans on Chinese open-weight models used by US businesses, would not address his main national security concerns. Instead, he pointed to the possibility of authoritarian governments surpassing the US in advanced AI, as well as cyber, biological, and alignment risks posed by increasingly capable systems.

Amodei also called for action against industrial-scale model distillation, which he said allows Chinese developers to improve their models with less computing power than would be needed to train comparable systems from scratch.

The statement followed criticism of Anthropic for not signing an industry letter backed by Nvidia, Microsoft, Meta, IBM, Mistral, Hugging Face and other technology companies urging policymakers to avoid premature restrictions on open-weight models.

The letter said that open weights could broaden access to AI, intensify competition, and enable organizations to adapt and deploy models without relying on a single provider. Amodei agreed with parts of that case but disputed claims that openness inherently improves safety research or gives defenders an advantage over attackers.

He said regulation should be based on a model’s capabilities and risks rather than whether its weights are openly available. Under that approach, sufficiently capable open and closed models would undergo testing before release.

Conditional support

Analysts said Anthropic had moved closer to industry consensus by rejecting blanket bans, but its support remained more limited than the approach backed by many major technology companies.

Deepika Giri, head of research for AI, analytics, and data at IDC, said the Nvidia-backed letter presented open weights as strategic infrastructure that should remain broadly accessible, in contrast with Anthropic’s more restrictive position.

Amodei’s statement clarified that Anthropic supports open-weight models only under certain conditions, a stance that could also help the company preserve its competitive advantages as a proprietary model provider focused on compliance and tighter controls, according to Lian Jye Su, chief analyst at Omdia.

The statement was “a real olive branch” to supporters of open-weight models, according to Pareekh Jain, CEO of Pareekh Consulting. But he said the disagreement had shifted from whether such models should be released to where policymakers should draw the line.

“Anthropic still thinks that once a model gets powerful enough, releasing its weights publicly is riskier than keeping it locked behind an app, because you can never take it back or add safety fixes later,” Jain said.

Will the controls work?

Analysts differed over whether Anthropic’s proposed controls would achieve their aims without creating new barriers for smaller AI developers.

Jain said chip restrictions and measures against illicit model distillation would mainly affect model developers and infrastructure providers, rather than enterprises using models already on the market. Mandatory safety testing, however, could raise development costs and reduce the number of advanced open-weight models available.

“Testing is expensive and time-consuming, and so, giant, well-funded companies like Anthropic, Google and OpenAI can afford it,” Jain said. Smaller developers seeking to release cutting-edge open-weight models could struggle to meet the same requirements, he added.

The additional testing and screening could also restrict the number of open-weight models available to enterprises, according to Su. He said the requirements could weaken some of their principal benefits, including lower costs, reduced vendor dependence and community-led development.

Anand Joshi, managing director of market research firm JP Data, questioned whether limiting China’s access to advanced chips would materially slow its AI development, arguing that Chinese companies had shown they could build highly capable models with less computing power. He supported action against illicit distillation, however, saying safeguards were needed to prevent developers from reproducing the capabilities of other models without authorization.

The impact on most enterprise users could remain limited if less capable models were exempted, Jain said. Businesses deploying models that fall below the proposed testing threshold would probably face little additional cost.

How CIOs should choose

Giri said CIOs should assess models according to their capabilities rather than whether they are open, and should demand independent testing, clear licensing, model documentation and accountability for monitoring and incident response.

“Mandatory safety testing should be triggered by a model’s demonstrated capabilities, not its size or training cost,” Jain said, particularly when a system could significantly assist cyberattacks, biological misuse, or autonomous harmful actions.

Before deployment, CIOs should seek independent evaluations, detailed model documentation, security test results and information about the model’s software supply chain, he added. Charlie Dai, principal analyst at Forrester, said that assessment should include documented red-team results, model provenance, disclosures about training and fine-tuning, and evidence of independent testing against recognized safety benchmarks.