According to Saxo Markets, stock investors looking to capitalize on the red-hot artificial intelligence theme should look to Asia, where valuations for U.S. tech companies have become stretched.
“Asia offers a cheaper and more earnings-anchored path into the same mega-trend,” Charu Chanana, Saxo’s chief investment strategist in Singapore, wrote in a report. “Roughly 70% of global chip manufacturing, 90% of AI memory, and nearly all advanced packaging capacity are located in Taiwan, South Korea, and Japan, making the region indispensable to the AI ecosystem.”
She added that in contrast, U.S. tech stocks are trading at high valuations, with companies facing various risks such as concentration and closed-loop dynamics. Data shows that the S&P 500 Information Technology Index trades at a forward price-to-earnings ratio of nearly 30, compared with 17 for the MSCI Asia Pacific Information Technology Index. Saxo’s comments come as concerns grow among investors about the lofty valuations of some of the biggest AI winners, particularly U.S. innovation leaders like Nvidia. Recently, a wave of closed-loop trading involving OpenAI, Nvidia, and other AI-focused firms has further fueled worries that the AI boom may be artificially inflated.
Chanana wrote in the report that although Asia-based AI-related companies also face “global cyclical risks,” they offer better earnings visibility due to significant capital spending flowing into Asian supply chains for AI infrastructure buildout.
She also noted: “The physical expansion of AI infrastructure — chips, servers, data centers — is still full steam ahead, and a large part of that is happening in Asia.”

Define the production contract first

Agree on expected quality, latency, cost and failure behavior before optimizing individual components. A subsystem can look healthy while violating the end-to-end user objective. Make state ownership explicit.

Models, queues, caches and databases often hold overlapping representations. Each durable state should have one authoritative owner and a version so stale copies cannot silently win.

Test degradation and recovery

Simulate unavailable dependencies, stale caches and overload. Production systems need predictable fallbacks and a clear path back to normal operation rather than manual repair for every incident. Define the production contract first.

Agree on expected quality, latency, cost and failure behavior before optimizing individual components. A subsystem can look healthy while violating the end-to-end user objective.

Make state ownership explicit

Models, queues, caches and databases often hold overlapping representations. Each durable state should have one authoritative owner and a version so stale copies cannot silently win. Test degradation and recovery.

Simulate unavailable dependencies, stale caches and overload. Production systems need predictable fallbacks and a clear path back to normal operation rather than manual repair for every incident.

Define the production contract first

Agree on expected quality, latency, cost and failure behavior before optimizing individual components. A subsystem can look healthy while violating the end-to-end user objective. Make state ownership explicit.

Models, queues, caches and databases often hold overlapping representations. Each durable state should have one authoritative owner and a version so stale copies cannot silently win.

Test degradation and recovery

Simulate unavailable dependencies, stale caches and overload. Production systems need predictable fallbacks and a clear path back to normal operation rather than manual repair for every incident. Second-order effects can matter more than the announcement.

Technology moves often affect suppliers, competitors, developers and customers as much as the company named in the headline. The practical question is whether the event changes cost, capacity, standards, distribution or strategic options. Long-term scenarios should be evaluated against near-term milestones so ambitious forecasts remain clearly separated from confirmed execution.

How to read the development beyond the headline

The reported development is most useful when separated into confirmed facts, forecasts and interpretation. Product timelines, pricing, supply constraints, regulation and customer adoption can all determine whether an early headline becomes a durable shift. Readers should update the thesis as concrete follow-through appears instead of treating an initial announcement or market reaction as the final outcome.

What to watch next. Useful signals include shipped products, independently observed performance, customer commitments, pricing changes, financial guidance and regulatory decisions. Those indicators show whether the development is changing real operations rather than only sentiment.

Practical implementation check

Before applying this idea broadly, define a production baseline, the expected improvement and the failure conditions that would trigger rollback. Test with realistic traffic or representative workloads instead of relying on one isolated demo. Document ownership, monitoring and the assumptions that may become stale. This makes later changes easier to evaluate and prevents a one-time optimization from becoming an unexamined permanent dependency.