The numbers are impressive. From December to May, AI-related IPOs in Hong Kong raised nearly HK$100 billion—roughly 55% of total listings during that window. Exports have posted double-digit growth for several consecutive quarters, driven by what the Financial Secretary calls "robust global demand for AI-related products."
Logic does not bleed, but code leaves traces. And in this case, the traces point to a fundamental question that the official narrative carefully sidesteps: What exactly is being counted as "AI" in these figures?
The definition of "AI-related" in an IPO context has always been generous. It is an elastic term, stretched to accommodate any company with a data team or a machine-learning slide in its pitch deck. During the 2021 SPAC boom, we saw EV companies with no vehicles and battery startups with no cells raise billions. The current AI wave carries a similar structural pattern, and Hong Kong is positioning itself as the primary liquidity pool for it.
Here is what the official narrative gets right, and where it begins to fray under inspection.
The architecture of the play
Hong Kong's strategy is not complicated. The government is signaling that it wants to be the world's AI IPO venue of choice. It is a market-maker play, not a technology play. The city is not trying to build foundational models or claim breakthroughs in algorithmic research. It is offering what it has always offered: deep capital pools, common law, free capital movement, and a strategic position between Mainland China and global markets.
This is not a criticism. It is a legitimate economic position. But it comes with a specific vulnerability: your asset class is only as strong as the underlying fundamentals of the companies you list. And the underlying fundamentals of the current AI IPO cohort are, in many cases, not strong.
In my 2020 audit of a yield aggregator that drained $30 million in user funds, the pattern was the same. The narrative was impeccable. The architecture was flawed. The team was not necessarily malicious. They were simply building on assumptions that had never been stress-tested. The structure failed because it was not designed for adversarial conditions.
The 650 billion question
Let us focus on the figure that anchors the government's entire argument: HK$65 billion in potential economic benefits if SMEs adopt AI at the same rate as large enterprises by 2035.
This number comes from a research report, and it assumes a linear diffusion curve. It assumes that SMEs have the capital to invest in AI infrastructure, that they have the talent to implement and maintain these systems, and that they have the data quality to generate meaningful outputs. All three assumptions are generous.
According to my experience in the 2017 ICO period, when I analyzed 45 whitepapers for projects raising over $2 million each, the most consistent failure mode was not in the vision. It was in the tokenomics—the assumptions about supply, demand, and velocity that were never tested in the real world. The same failure mode appears here. The report assumes the benefits without modeling the costs of adoption. The cost is not just the software license. It is the organizational restructuring, the training, the process redesign, and the opportunity cost of failed implementations.
The efficiency improvement group has delivered 30 projects across 13 departments. This is a meaningful start. It proves that the government can implement AI internally. But government efficiency is not a proxy for SME readiness. The public sector has different incentives, different risk tolerances, and different failure costs.
The talent bottleneck
Hong Kong has a structural problem with AI talent. The city has excellent universities, but it is a small ecosystem. A concentrated AI cluster requires a critical mass of researchers, engineers, and product managers. Hong Kong is still importing that talent.
The "high-end talent pass" scheme has helped. But the numbers are not yet sufficient. The city is building a factory without enough skilled workers.
The capital is present. The market signal is strong. The government is supportive. But the human infrastructure is still under construction. This is the missing variable in the official calculation.
What the bulls get right
I am not in the business of dismissive analysis. The contrarian angle here is that the bulls are not entirely wrong. In fact, they are right on a fundamental point.
Hong Kong has a unique position. It is the only jurisdiction that offers deep Western-style capital markets with a direct on-ramp to the Chinese economy. Singapore offers some of this, but not the same access. The valuation gap and the regulatory gap between mainland and international markets are precisely where Hong Kong's role as an intermediary is valuable.
The AI IPO numbers are not artificial. The capital is real. The export data is not a phantom. The global demand for AI hardware and solutions is not a speculative construct. It is a real industrial trend. The government's decision to push forward with AI adoption is rational.
What is less rational is the assumption that the trend will continue in a linear fashion without a correction. The global AI market is in a price discovery phase. When the interest rate cycle normalizes and the growth premium compresses, the AI IPO will face the same re-rating as the internet stocks of the dotcom era. The 55% of IPO volume is a sign of a market peak, not a market base.
The data cross-border dilemma
The more subtle issue is data governance. Hong Kong's position as a data hub depends on its ability to move data across borders. The legal framework for this is not fully developed. The mainland's data rules are different from Hong Kong's. The EU's AI Act is a risk-based regulatory approach that will have extraterritorial implications for any company operating in Europe.
This is a governance problem, not a technology problem. The government's speech did not mention it. This is an intentional silence. It is a reflection of the "grow first, regulate later" approach.
I have seen this approach in the 2022 Terra/LUNA collapse. The algorithmic feedback loop that led to the $40 billion loss was not a technical bug. It was a design assumption that the system would maintain its peg under stress. The assumption was false. The same assumption underlies the current AI market: the assumption that the adoption curve will be smooth, and the regulatory risks will be manageable.
What needs to be watched
The key signal will be the second batch of the efficiency improvement group. If the next round expands to cover more sectors with measurable outcomes, it will indicate that the government's approach is more than a policy. If the AI IPO volume continues at the 55% level through the end of the year, it will indicate a structural shift in the capital market.
The key risk is a talent gap. I will be watching the enrollment numbers in AI-related university programs and the success rate of the talent pass. I will also be watching for announcements about computing infrastructure. If Hong Kong plans to build a large-scale AI data center, it will be a signal of long-term commitment. If it continues to rely on external cloud capacity, it will be a signal of dependency.
The takeaway
Hong Kong's AI strategy is not a scam. It is a strategy. It is a rational response to a global trend, positioned within a jurisdiction with unique advantages. The warning is the assumption that the current trends will continue without resistance.
The $100 billion IPO figure is a flow, not a stock. It reflects a market cycle, not a market base. The 55% share is an indicator of momentum, not of permanence. The 30 projects are a proof of concept, not a proof of scalability.
The rug is not pulled; it was never tied. The question is whether the foundations are real enough to support the structure when the market turns. That is not a question that can be answered in a policy speech. It can only be answered by the data that will emerge over the next 18 to 36 months.
Imagination is infinite, but liquidity is finite. The market will eventually price the gap between the two.
Gas fees are the price of truth. And the truth is that Hong Kong has a window of opportunity, but the window is not guaranteed to stay open. The question is not whether the government can promote AI. The question is whether the ecosystem can deliver the talent, the data, and the adoption to justify the capital that is already been deployed.