Sovereign Gold: What Bangladesh Can Actually Own

Artificial Intelligence means different things to different people.

Higher revenue for a businessman. Something incomprehensible to your mother. A way out of writing documents for a friend who is too lazy to write.

To a government, it means a harder question: Who else can switch it off?

AI sovereignty is usually defined as the ability to build and govern a nation’s AI technology stack without depending on foreign entities. The French government actively uses Mistral AI, a French company seen as the flag-bearer for European sovereignty.

But beyond the US and China, no one is building at the frontier. The infrastructure is too expensive, the technology is moving too fast, and the expertise is scarce everywhere.

Governments want it anyway, to reduce dependence, for national security, and to keep the earnings at home rather than watch them flow out.

We saw the same during the rise of the internet, where consumption in national economies enriched American tech firms.

For Bangladesh, building that stack means a long list with a massive price tag, competing with an energy crisis and geo-political volatility.

But there is another side that people barely talk about. Who will build and manage sovereign AI? What skills does that require, and what does managing a fast-moving, uncertain technology actually entail?

The Test

Before any of that can be answered, the definition needs work. Measured as independence, sovereign AI is out of reach for almost everyone this decade, Bangladesh included. Pretending otherwise leads to policy documents instead of real capability. Sovereignty is control. The ability to decide what runs, on what terms, and to keep deciding when someone else changes the rules.

Mistral’s Arthur Mensch put it plainly in an interview with the venture firm Andreessen Horowitz: “If you assume that the entire economy is going to run on AI systems, enterprises will just want to make sure that nobody can turn off their systems.”

This stopped being hypothetical in June 2026.

The US government barred foreign nationals from using Anthropic’s most advanced models, and Anthropic suspended their access.

Overnight, every foreign organization, ministry, and university that had built on these models found that someone else controlled the terms of its operations. There was no hacking; no contract was breached. A policy had changed, and their systems stopped working.

You can rent compute, you can download models, and you can eventually buy chips, but you cannot quickly “procure” a population that understands these systems well enough to notice when rules change, judge what that means, and rebuild on something else.

This is sovereign gold, the only layer of a country’s stack that it builds and accumulates rather than purchases. It is the only one that cannot be switched off by external power.

The Work

Assume Bangladesh solves the expensive part: The electricity deployed, the land allocated, and the hardware and chips procured. What remains is skilled people who can build the AI models, create applications, and manage risks over time.

Building Models

Open-source AI models are a cost-effective way of building new AI models without cloud dependence. They let countries create their own applications on their own terms. Bangladesh needs people trained to build these models to meet sovereign needs and support service delivery.

Building Applications

AI applications are part of our daily lives, from ChatGPT to Claude. Entrepreneurs, university students, and researchers are already building them. At the sovereign level, the work requires consistency, stronger risk management, and maintenance.

Keeping Up

The pace of releases is outrunning anyone’s ability to assess them. In July 2026, OpenAI’s autonomous agents caused a high-profile cyber incident at Hugging Face, another AI firm. Nobody ordered it, which is the part worth noticing.

The acceleration of AI developments happening overseas is important to track for developing models, modifying them, and anticipating risks. At a sovereign level, the risks are higher, and the appetite for risk is lower than among private entities.

Two Precedents

No country has built all of this. The ones that got somewhere chose a layer and committed to it.

Taiwan chose to fully own one layer of the stack: Chip manufacturing. Go deep and go long. A state research institute, a science park, and a policy set in the 1970s produced, five decades later, a single firm, Taiwan Semiconductor Manufacturing Company (TSMC), that makes most of the world’s advanced chips, and a country that Jensen Huang, Nvidia’s boss, calls “the epicenter” of the AI revolution.

Note what Taiwan lacks: Recognition as a sovereign state by most governments. What it has instead is leverage.

Singapore took the other route. It decided it did not need to own the stack but would own the talent and the terms. Its AI Apprenticeship Program pays citizens a stipend of SGD 4,000 a month for six to nine months to build production AI systems alongside experienced engineers, placing more than nine in ten of them in AI roles within half a year.

That is not a training scheme. It is a salary, paid by the state, to manufacture engineers. Around it sits a governance framework that sets the terms under which foreign models and foreign compute operate in the country.

Bangladesh cannot be Taiwan. There is no fifty-year runway and no chip industry to build on. It can be Singapore, on a smaller budget, with a much larger population, and with the same basic decision.

Three Horizons

For Bangladesh, that decision breaks into three horizons. All three begin now. They differ in when they pay off, not in when they start.

Immediate: The People

Bangladesh needs people who can fine-tune, adapt, and deploy, and it needs them first. In a recent report, The Economist flags these as the slowest to build and hardest to buy.

The fastest route is the one Singapore took: Paid apprenticeships on real work, open to new graduates and existing professionals alike.

A stipend is what gets people to finish. The work itself can be national. A public-private consortium fine-tuning open-source models on Bangla data, with a deliverable and a deadline, would give apprentices a real problem and give the country a real asset.

State-funded certification through the National Skills Development Authority (NSDA) and the Ministry of Education, covering model building, applications, and safety, can mark completion and qualify officers for deployment inside the government.

None of this requires waiting for a new university curriculum. 

It requires financing, academic partners, and knowledge transfer from abroad, organized one cohort at a time.

Mid-term: The Institutions

Reskilling can produce a cohort, but it does not create a sustainable supply. Between the first trained batch and a national capability lies the hard, unglamorous work: Academic departments that can teach without waiting for a foreign grant, labs with enough computing power for students to learn on real problems, certifications that mean something to an employer, and a career path that makes staying in the country a rational choice rather than just a patriotic one.

Universities abroad can shorten that work. The Ministry of Education can build joint programs with them, and non-resident Bangladeshis in foreign academia are the natural bridge. Some of this has started. Purdue and Arizona State took part in the National Semiconductor Symposium in Dhaka in July, and Bangladesh has begun exploring a partnership with Arizona State in semiconductors and AI.

A credential recognized by industry and enforced in public procurement turns training into hiring. And public-sector pay has to match the work. Otherwise, the government trains the engineer, loses him, and rents him back through a vendor at several times the cost.

Long-term: The Generation

In the long run, the work to build capacity will need to start in schools. Curriculum upgrades and teacher training come first, because a student taught mathematics and computing poorly at fourteen is rarely building models at twenty-four. The goal is not more coding camps. It is people in law, health, journalism, and agriculture who understand what these systems can and cannot do in their own fields.

At the top of the pipeline sits the scarcest input: People who can supervise research. Experienced supervisors take longer to produce than any machine the country will buy, and without them every promising doctoral student still has to leave to be trained. Knowledge exchange and partnerships with universities can begin to close that gap.

The systems these people build must also work in Bangla and serve the country outside Dhaka. Done steadily, this is the domestic pipeline that stops the country from permanently renting talent the way it currently rents compute.

Across all three, the policy implications are the same: Resource allocation, enforcement, and a financing model.

The Case

None of it works on a single government’s timeline, and that is the hard part. The work itself is entrepreneurial in character: Quick adaptability, anticipating risk, high uncertainty, and no settled policy framework to lean on. Sovereignty cannot be run that way. It has to be systematic, bureaucratic, and built to outlast the government that starts it.

That means an institution with a name, a budget line anyone can read, and a public report against it every year. Bangladesh has never been short of vision documents. It has been short of the machinery that carries one past the next election. This needs resourcing now, even if it pays off in decades.

The country doesn’t need to own its AI stack; the economics rule that out. It needs people who can keep it running on its own terms. Sovereignty is not what you own. It is what still works when someone else changes their mind.

Dr. Zunaid Kazi is co-founder and CTO of Gofai, a neurosymbolic AI platform, and founder of Knowtomation, an AI advisory practice. He has worked in AI for more than 30 years, including at IBM’s T. J. Watson Research Center.

Sadman Rahman is the lead author of the draft Bangladesh National Artificial Intelligence Policy 2026-2030. He is a policy analyst, advisor, and musician.