[🇧🇩] Artificial Intelligence-----It's challenges and Prospects in Bangladesh

[🇧🇩] Artificial Intelligence-----It's challenges and Prospects in Bangladesh
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AI: is science fiction becoming real?


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A humanoid robot takes selfies with a visitor at the 7th World Voice Expo in Hefei, east China's Anhui Province, October 24, 2024 — Xinhua Photo

In 2001: A Space Odyssey, Arthur C Clarke showed how the smart computer HAL9000 disobeyed command from its astronauts and killed one of them to scupper their plan to return from space. Isaac Asimov in his science fiction I, Robot lays down three principles regulating the robots which emphasise robots will not harm human beings under any circumstances. Regardless of restrictions imposed, one robot in his book escapes the system and runs amok.

Recent developments with Artificial Intelligence (AI) indicate that the science fiction stories may become facts in real life soon. First, AI model agents escaped the laboratory of Google during a cyber security evaluation and hacked the accounts of three companies after unintended access to internet. Second, one of the senior AI researcher of Anthropic (a US-based AI technology company), Jacob Coxon, recently resigned from his post and publicly warned that AI companies were racing straight to self-improving super intelligence and that consequences could be catastrophic for humanity. In an interview with Bloomberg he said that sufficiently advanced AI could potentially become capable of killing human in the next decade and that society could not simply protect itself by choosing not to use AI. There is also a warning from another former Anthropic safety employee, Joe Benton, who left shortly after Jacib Coxon and issued similar grave concerns.

There has been an extraordinary expansion of AI in recent years from one where it answers questions to agents capable of taking actions on their own in the real world. The issue is no longer merely whether AI can produce an answer but whether an AI system can independently pursue an objective through the internet and computer systems.

To understand the magnitude of the problem a brief review of the evolution of AI will be in order. Three phases of AI are be discussed below :

(1) Traditional AI answers a human’s questions by using the large language model (LLM). (2) Generative AI produces text, images, code, analysis etc. (3) Agentic AI can plan, use tools, access the internet, execute commands and pursue a goal with less or no human oversight and intervention.

The significance of the recent incidents mentioned above is however not that AI has suddenly become ‘evil’. Rather, the point to note is that greater autonomy creates the possibility of unintended consequences because of machine scale and speed. This provides the bridge to the warnings from the whistleblowers inside the industry. The broader concerns within AI safety research identified by the critics are : loss of human control, autonomous cyberattacks, increasingly capable AI systems improving or assisting the creation of successor systems, misuse by states, corporations or individuals, autonomous weapons, biological/ chemical and other catastrophic misuse. The extreme scenario apprehended by AI researchers is the extinction of human race at the hands of super smart AI models. Apart from the mid (ten years) to long term existential threat of AI the persistent criticism about the downsides of the use of AI in industry, business and services sector have been about substitution of machines with AI of works traditionally done by human labour, physical and mental.

The employment consequences of AI are already visible, although it is important to make a distinction between jobs lost because of AI and layoffs occurring because of technological transformation without AI. According to a US consulting firm, Challenger, Gray and and Christmas, there have been 112,713 job cuts in the first seven months of 2026, accounting for 24 per cent of all job cuts during that period.

Since Challenger began separately tracking AI as a factor for layoffs in 2023, it has recorded 184,538 job cuts due to AI. Complete count of jobs eliminated worldwide has not been made and so the above figures refer only to the case of America. The long- term picture is more complicated. The World Economic Forum’s ‘Future of Jobs Report 2025’, based on employers’ expectations rather than observed job losses, projected that technological and other economic changes could displace 92 million jobs globally by 2030, while creating 102 million new ones, producing a projected net increase of 10 millions.

It also found that 41 per cent of employers expected to reduce their workforce as AI automates certain tasks. Thus the available evidence points to significant job loss in the near future as a result of AI, particularly in some technology-intensive occupations. But the larger question is whether AI will ultimately destroy more employment than it creates. Equally important is the question whether workers displaced by AI can acquire the skills required for the new economy.

The common experience of all countries is that as a result of use of AI the entry level jobs in almost all developed economies have been wiped out. Equally affected have been people engaged in part-time work done online in so called ‘gig economy’. This year has brought a reckoning for while collar gig platforms like Fiverr and Upwork, which connect companies to freelancers all over the world to complete well-defined projects such as copywriting or graphic designs. It turns out that by breaking jobs into parts that are performed remotely, stripping them of the need for institutional knowledge or personal relationships, they have made that work particularly vulnerable to being replaced by AI. Fiverr reported a ‘clear deceleration in overall market place traffic and demand’ in the second quarter of the current year.

The firm attributed this in part to ‘recent model updates across various LLMs,’ and said, ‘put simply, AI is automating simple tasks’.

In last June, the head of China’s biggest commerce groups warned that its 700,000 delivery workers would be replaced by robots, sooner or later. In the United States (US) and China, drivers on platforms such as Uber and DIDI eye the arrival of self-driving cars with deep unease.

Gig platform have become a safety net for people who have fallen out of or struggled to access the formal labour market. A report by World Bank in 2023 estimated there were between 154 million and 435 million online gig workers, globally, representing between 4.4 and 12.5 per cent of the global labour force. Among the advantages of this development, according to this report’s authors, are that it helps manage income shocks and serves as unemployment insurance. So, what happens when the shock absorber gets hit with a shock of its own? The report asks. Because nobody employs gig workers, there is nobody to fire them either. Instead, the impact is likely to unfold under the radar in the form of fewer tasks and lower incomes, rather than the sort of job losses that usually traces.

The gig economy was never a great safety net, since it left people with no employment rights or protections, and patchy support from government in terms of protection and benefits. But now there is a new danger: robots are quietly replacing humans. Policy makers are not noticing this because it is happening under their radar.

A recent International Monetary Fund IMF) report has said that AI could lift European productivity by about one per cent over five years but risks widening inequality, straining power networks and increasing dependence on foreign technology unless European Union (EU) deepens economic integration. The report prepared for a meeting of EU finance ministers in Dublin on 18-19 September, said the benefits and costs of AI are likely to be distributed unevenly across countries, regions and workers. The report estimated that around 60 per cent of workers in advanced European economies are employed in occupations highly exposed to AI. While some could become more productive through AI tools, others faced displacement as routine tasks become automated, particularly in jobs where AI is more likely to replace labour than complement it. The report said that Europe’s data centres serving AI already consume 3 per cent of the continent’s electricity and that demand would rise sharply as AI use expands. The report also warned that Europe risks developing another strategic dependence because the US and China dominate the development of AI models.

The report’s indication about productivity increase and warning about growing use of energy in data centres and uneven benefits of AI across classes apply to all countries, while dependency on foreign technology refer to all countries except America and China. The benefits of use of AI in various sectors like industries, medical care, education, research and education are by now well documented as are its negative economic impacts. So far the balance of costs and benefits has been shown to be tilted toward the latter. But the latest revelation about existential threat to humans posed by AI has presented a problem that cannot be rationalised. The fact that the warning has come from insiders make the prospect of the doom’s day very much credible. Even the heads of leading tech giants have admitted that the dire consequences predicted by insiders can become reality in future. While announcing the rolling out of a new AI model to counter OpenAI’s momentum since its launch of GPT-6, the CEO of Anthropic, Dario Amodei, called in a paper written by him, on all the global AI community to slow down the pace of releasing new capabilities to address safety concerns. Dario Amodei’s paper painted a bleak picture of swarms of AI agents, taking over the internet and outpacing human control. His prognosis drew support from OpenAI CEO Sam Altman and SpaceX CEO Elon Mask.

The Financial Time editorial Board wrote recently: “There is a Jekyll-and-Hyde quality to the world’s most powerful AI bosses. The same executives now talking about slowing the pace of work on cutting-edge models given the risks to humanity have been competing hell-for-leather to advance AI, chasing trillion-dollar flotations. But the backing of two AI titans — Sam Altman of OpenAI and Elon Musk of SpaceX — for the slowdown call by a third, Dario Amodei of Anthropic, ought to become a watershed moment for the industry.” ( The FT View, Sept 14, 2026) Financial Times believes such co- operation among tech giants are unlikely but the slowdown calls suggest the companies realise it is time to act. ‘”They should use their power to slow, or better still to pause development of the most advanced frontier models until the risks can be understood and contained,” the FT editorial argued.

President Trump described the doomsday scenario as a ‘hoax’ and dismissed the calls for slowing down AI as ‘negative forces’, insisting America must beat China in the AI race. He recently announced the setting up of a ‘AI Force’ and appointing a ‘Czar’ to head the body. China is also interested in boosting development of AI in the interest of economic growth and modernising military technology. But instead of going alone it believes in multi-lateral co-operation and co-ordination to avoid risks. During his recent state visit to Washington President Xi reiterated his belief in mutual co- operation with America on AI development and international regulation of the advanced models. According to another FT Editorial Board write up, the US officials think the Chinese seem less concerned about AI’s risks to humanity than about its risk to Chinese Communist Party control. The FT Editorial Board believes for all their mutual suspicions Washington and Beijing have shared vulnerabilities in which it would be in their interests to co-operate and which could form the basis for the AI dialogue between the two superpowers.

So long academia warned about the risks of untrammelled progress and use of AI. They have not been taken seriously so far. Now that insiders from within the AI industry have become whistleblowers and bosses of leading AI companies have admitted the risks of runaway breakthroughs in the cutting-edge technology the race to the bottom may be reined in. Policy makers cannot afford to see science fiction becoming real.​
 

Bangladesh to launch sovereign AI within a year

Star Business Report

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Bangladesh plans to launch its sovereign artificial intelligence (AI) infrastructure within the next 12 months and make it available to the private sector, startups, universities and students, said Rehan Asad, adviser to the prime minister on ICT and telecommunications.

The infrastructure will allow local developers to build large language models (LLMs) in Bangla and develop AI-based services, he added.

“Most of the major global tech partnerships are already at a mature stage. We have secured the sourcing of computing chips,” Rehan said at the AI and Innovation Dialogue jointly organised by the Ministry of Foreign Affairs and the Governance Innovation Unit of the Prime Minister’s Office at the foreign ministry in Dhaka yesterday.

“We will launch our sovereign AI infrastructure within the next 12 months and open it to the private sector, startups, universities and students to build future LLMs in Bangla and develop innovative services,” he said.

The government has opted for a small, modular facility instead of building a large AI centre from the outset, Rehan said.

If demand from the private sector is strong, locally owned companies could co-locate at the facility and use its computing infrastructure, he added.

The government is also working to secure high-end graphics processing units (GPUs), which are needed to train and run advanced AI models.

Rehan said Bangladesh would be able to secure the world’s best GPUs for government data centres and Bangladesh-owned private companies through ongoing government-to-government discussions.

Securing high-end GPUs can be difficult, as delivery times can stretch to several years even when buyers have sufficient funds, he said.

“We will not face challenges in securing GPUs very soon,” Rehan said, adding that work was underway to ensure access to advanced computing hardware.

The government is also working to secure memory chips and plans to hold discussions with major industry players, with the aim of ensuring adequate supplies of both computing and memory chips for the next decade.

POWER, WATER CONCERNS

Power and water availability will be major considerations in developing AI infrastructure, Rehan said.

A conventional Google search uses about 0.3 watt-hours of electricity, while an AI-generated response on current platforms can use around 2.9 watt-hours, he said. This means AI computing is much more power-intensive.

Although AI systems are expected to become more efficient as the technology advances, overall electricity demand will remain a concern, he added.

Water availability is also important because AI infrastructure requires significant cooling capacity.

The government is therefore considering both power and water availability when selecting locations for AI facilities, Rehan said.

He added that the facilities would have multiple power sources and their own power-generation capacity instead of relying entirely on the national grid, as uninterrupted power is essential for such operations.

Ashfaq Zaman, founder of Dhaka Forum, said Bangladesh already has the basic building blocks of a digital state, including biometric NIDs, mobile financial services, a national QR code standard, around 300 million archived Bangla documents and D-Nothi across 17,000 offices.

“The real constraint is not the lack of systems but their failure to connect. AI can help bridge this gap. A single Bangla large language model for the government, trained on public-sector documents and hosted domestically, could help officials review drafts instead of starting from scratch,” said Ashfaq, who delivered a keynote at the event.

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If each officer saves 20 minutes a day, 250,000 officers could save about 10,400 working years each year, he said.

Such gains should be measured through public-service targets covering time, cost and the number of interactions required, turning AI into a measurable governance reform, Ashfaq said.

He added that this would allow AI adoption to be assessed based on measurable improvements in public services rather than simply treating it as an information technology project.

Md Ismail Zabihullah, adviser to the prime minister on public administration affairs, said AI is no longer just a policy ambition but is becoming a practical tool for transforming public administration.

AI-enabled recruitment and postings, along with a unified digital identity and wallet, can make government services faster, more transparent and more citizen-focused, he said.

At the same time, strong data protection and cybersecurity must remain a priority.

Bangladesh should also move beyond being a technology consumer and build the capacity to develop, own and export high-value technology products and AI-based services, he added.

Humaiun Kobir, state minister-1 at the Ministry of Foreign Affairs, and Shama Obaed Islam, state minister-2, also spoke.​
 

Is AI weakening young minds?


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"Miss, do you know, these answers can be sorted easily?"

"Seriously, how?"

"My teacher at school told me that I can complete my homework from ChatGPT. It gives all the answers, and it is quick."

I paused. These words were uttered by my 12-year-old student during a private tutoring session, who studies in one of the reputed English version schools.

If a child is encouraged to use artificial intelligence (AI) at this young age, simply because it provides answers faster, I cannot help but to simply ask, where is the education heading to?

Technology has always evolved and with that we have learned the way to communicate with it. While employing technology in our daily lives, what we make sure is that, we are utilising it and it is not using us. When a child tells that it gives quick answers for their homework, what concerns the most is that, are these children having the opportunity to struggle with the question?

If a child's cognitive development relies on a machine, especially at the age of twelve, their ability to question, reason, interpret, express an opinion and communicate an idea will gradually compromised.

This narration doesn't really establish the fact that we must stop handing them over technology. Rather, we must challenge them to ensure that it expands their ability to think instead of them getting replaced by it. A world which is AI-driven and advancing, the purpose of education should be to make children technologically capable and intellectually independent, not making them dependent on technology.

Norway in 2024, banned using smartphones in classroom, and it successfully found positive results as students were more engaged, social and reduced online bullying. Furthermore, from Autumn 2026, Norway recommends that students should not have access to generative AI in their primary schools. Students from grade one to seven are restricted using AI, while for grade eight to ten students they can gradually and cautiously use it. By the time students are in upper secondary schools, they should be prepared to utilize AI that will prepare them for higher education and work.

According Norwegian PM Jonas GahrStøre, technology risks developing foundational steps for children especially in learning to read, write and for basic mathematics. It also declines in critical thinking. When children get depended on finding instant answers, their problem-solving skills, logical reasoning or the ability to analyze complex issues independently, will start to stagnate.

However, Poland had a different approach. The government planned to equip 12000 AI Labs in their primary and secondary schools. As per Polish PM Donald Tusk, children must learn to control AI than being controlled by it. The crucial responsibility for the younger generation would be to have sense of control over technologies they utilize, and not to underestimate the risk comes with it.

How developing countries can utilise it without compromising on young minds?

Education system around the world must not choose between restricting AI and embracing it. In fact, it must develop a framework: protect, prepare, practice and apply. The first stage of learning is to protect children's foundation which is built through years of reading, writing, mathematics and problem-solving skills without relying on generative AI.

Preparing students how to use technology is the second stage. Here they will understand that technology fails and verification about an information is important. In this phase, they will understand digital world and AI. In the third stage practice begins. This is where students would question an argument, they must compare perspective and challenge their own reasoning then simply getting an answer. Lastly, they can begin to apply higher-level projects, research and creative work while they will still continue to think, decide and communicate the final outcome.

This approach will make children technologically capable without becoming intellectually dependent, ensuring that AI strengthens their learning rather than replacing the process through which learning takes place.

As my student addressed about the way he is using AI to get his school homework done, he is skipping his productive struggle and getting fewer opportunities to practice reasoning. Reasoning are usually expressed through communication. Hence when a child gets himself over dependent on AI at the age of twelve, it will affect their communication development. For this case, teachers must protect independent thinking before introducing AI and then it can be taught as a questioning tool, verification and exploration.

If teachers start handing over AI to children for homework, how will these children be able to compete with AI, if they rely on AI? A child who never practices to solve a question by himself, will he be able to formulate an idea?

While in primary schools, children are still learning to read, write, understand grammar, ask questions and solve mathematics. Teachers must establish syllabus that make children independent thinkers. Children from primary schools must not access AI, until they become expert in their basic learning and problem-solving skills. Teachers can use AI for appropriate planning and preparation, but it should not be used as a shortcut that transfers the learning task to the child.

Without making generative AI their first source of every answer, they can be introduced to use this tool to search for information, compare sources, identify misinformation, use spreadsheets and presentation tools. AI can be utilised to create digital projects, learn basic coding, multimedia content and ideas can be communicated through technology. These activities will continue to engage students and they will be encouraged to think, make decision and boost creativity.

AI is advancing with time. This technology cannot be eliminated from our daily lives, but for students, it can be utilised in ways that can develop their strong digital skills without relying on AI for every answer without compromising on their critical thinking and problem-solving skills. The main goal here is to keep children responsible to think, make decision and boost creativity, which can happen through these activities.

The principle should be simple: technology should assist the learner, not replace the learner.

The most concerning notion here is not about a child using AI. Due to its popularity, children will use and implement it in their daily lives. These days, this technology is becoming a necessity. This is something which cannot be eliminated. Perhaps the correct question to ask is that even if they use AI, what kind of learner they will become in the future?

Children may finish their homework in seconds. But their education won't be measured how quick the homework was completed. Rather, if they understood their lessons, are they developing the habit of asking questions, finding reasons, solving-problems, engaging in daily communication and if they are being able to make independent judgement. Hence, teachers at school must not make convenience important than learning. Before a child's foundational development, technology must not be introduced. Hence, the responsibility that arises for teachers is that technology must not replace human. Space can be created when children begin to think independently, use technology intelligently and return to human communication to explain what they have learned.

Our achievement here will not only be to teach children who can ask AI anything. They should be taught to know what to ask, why they should ask, how the information can be verified and if they agree to the result it has provided. For Bangladesh and other developing countries, children deserve education that creates a strong foundational ability which is required anywhere in the world. A future-ready child is not someone who is most advanced in technology but who can use without surrendering their independent thought on it. Thus, AI in education must be used ethically that creates future leaders.

Finding quick answer is not the solution for all the problems but struggling and spending time to find the right answer to become an independent thinker is the correct definition of education. Education is never meant to be a race. When children are given the opportunity to think, question and solve question for themselves, they build a lifelong stronger foundation. AI can accelerate learning. But it should never accelerate a child past the learning itself.

The writer is a media analyst.​
 

Navigating the US-China rivalry: Bangladesh cannot outsource its AI sovereignty

Ayesha Tariq

On the morning of August 1, the United States (US) Special Envoy for South and Central Asia, Sergio Gor, met Prime Minister Tarique Rahman in Dhaka and suggested that Bangladesh join Pax Silica, the State Department's coalition for artificial intelligence (AI), semiconductor and critical mineral supply chains. Later that day, the Ministry of Foreign Affairs announced that Bangladesh would join the Shanghai-based World Artificial Intelligence Cooperation Organization (WAICO), initially as an observer. Dhaka faces a diplomatic balancing act, even if it has made few concrete commitments so far.

The two blocs offer different things. Pax Silica, launched in Washington in December 2025, began with seven signatories, including Japan, Singapore and the United Kingdom, and had grown to twenty-five by August, according to the Information Technology and Innovation Foundation. It is widely seen as an effort to reduce reliance on China. WAICO, founded in Shanghai on July 16 by twenty-nine countries, including Russia and Pakistan, presents itself as a development-focused and more inclusive alternative. Its pitch is aimed at developing countries that regard Western technology controls as a barrier.

Dhaka has reasons to maintain close ties with both. China supplied between 28% and 30% of Bangladesh’s monthly imports in the last quarter of 2024, making it the country’s largest source of imports. The US, for its part, is the largest single destination for Bangladeshi goods. Beyond Washington's existing ties with Dhaka, Gor saw considerable potential in information technology, and the two sides discussed American investment in warehousing and agriculture, as well as financing through the US International Development Finance Corporation. Those talks point to a partnership that is broadening beyond exports, much as China’s role already extends across trade, industrial zones and river management, as demonstrated by the 13 memorandums signed during the Prime Minister’s visit in June.

For now, Dhaka can maintain ties with both blocs because it has made no binding commitment to either. That could change once each bloc establishes rules governing which vendors and data it trusts. A country whose systems can operate under either set of rules can negotiate over the terms, while one tied to a single supplier must accept the conditions set by that supplier’s bloc.


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As the US-led Pax Silica and China-backed WAICO compete to shape the future of artificial intelligence, Bangladesh faces the challenge of safeguarding its technological sovereignty while navigating the rivalry between Washington and Beijing. Illustration: AI-generated

The draft National AI Policy for 2026 to 2030 shows what sovereignty requires. It promises a national computing strategy with phased upgrades to high-performance computing infrastructure at the National Data Centre, and it requires sensitive datasets, including health, biometric, financial and national security data, to be stored and processed in Bangladesh or in jurisdictions designated as trusted. The policy has yet to be approved by the current government, but its proposed guidelines are sound. However, we must remember that computing depends on electricity, and a national data centre will compete for a power supply that must already serve homes, industries and hospitals. The energy crisis we faced this year made it particularly apparent that we need better planning and a more reliable electricity supply. A facility that cannot rely on the grid cannot host a state's most sensitive systems, so the final policy should link the computing strategy to an agreed electricity plan.

The hardware question leads back to geopolitics. Bangladesh does not manufacture advanced chips, so every processor in a national data centre will have to be imported through supply chains that one bloc or the other controls. The policy's reference to 'trusted' jurisdictions raises the obvious question of who determines which jurisdictions can be trusted, and Washington and Beijing will answer it differently.

The Philippines offers a warning about moving too quickly. Its planned economic security zone at New Clark City, covering 4,000 acres, could require up to three gigawatts of electricity at full development, according to estimates by the agency in charge. The mayor of Capas in Tarlac province has said that the plan was not formally presented to his local government, the Daily Tribune reported. Joining a coalition does not eliminate the need for the power supply, local consent and legal groundwork that a project of that size requires.

In the AI era, strategic autonomy is as much a matter of system design as it is of diplomacy. A government that can move its workloads between cloud providers and replace one AI model with another without rebuilding its services keeps its options open. A government whose public services depend on a single company's technology has effectively chosen a side.

The draft policy already recognises this, requiring public-sector AI systems to use open standards, interoperable data formats and portable models specifically to prevent vendor lock-in. The task now is to incorporate those requirements into every contract before the first large-scale systems are built, since retrofitting them later would cost far more.

In the AI era, strategic autonomy is as much a matter of system design as it is of diplomacy. A government that can move its workloads between cloud providers and replace one AI model with another without rebuilding its services keeps its options open. A government whose public services depend on a single company's technology has effectively chosen a side.

The choice of AI model is where this matters most. Open-weight models, which can be downloaded, hosted domestically and adapted using Bangla-language data, offer one of the most practical routes to developing systems that the state can control. However, open models also have national affiliations. Meta's Llama and Alibaba's Qwen are both freely available, yet a ministry that standardises on either one ties its developers and their tools to an ecosystem anchored in either Washington or Beijing. Dhaka should test competing models against the same Bangla-language benchmarks and ensure that its systems remain capable of running more than one model. The choice of model should be treated as a procurement decision that can be reversed when a better or safer alternative becomes available.

The second test of sovereignty is people. AI capability cannot be bought once and for all, because models are regularly replaced and the threats they address continue to evolve. A ministry that relies on foreign advisers and vendors to operate its systems holds a service contract, while the knowledge remains with the supplier. Without that knowledge at home, Bangladesh risks becoming a low-cost site for other countries' systems, with the valuable design work and profits remaining abroad, regardless of which bloc supplies the technology.

These issues matter most in national security. AI has made it cheaper to conduct disinformation campaigns and harder to verify what is real, and security agencies are likely to be among the government's earliest and most demanding users of the technology. They are also among the most likely to purchase complete systems from abroad, even though dependence on a foreign supplier in this sector carries particularly high costs.

The measure of progress should be how quickly Bangladeshi officials take over the work, and the vendor certification framework proposed in the draft policy is the natural place to make this a requirement. Every significant AI contract should set deadlines by which local staff must take responsibility for operating the systems and making technical decisions, with suppliers' payments tied partly to the success of that handover. Bangladesh has around 700 chip designers and produces more than 20,000 computer science and electrical engineering graduates each year, providing a potential talent pool for developing domestic AI capabilities. The state's challenge is recruiting and retaining enough of them by offering salaries that can compete with those in the private sector.


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US Special Envoy for South and Central Asia Sergio Gor meets Prime Minister Tarique Rahman at the Prime Minister’s Office in Dhaka on August 1, 2026. During the meeting, Gor proposed that Bangladesh join the US-led Pax Silica initiative, which focuses on artificial intelligence, semiconductors and critical mineral supply chains.

Over the next two years, both blocs are likely to move from broad declarations to specific conditions, attaching rules governing vendors, data and computing access to their offers of investment and technology. Bangladesh should retain its observer status in WAICO and continue discussions about Pax Silica, while refraining from fully joining either until it can offer capabilities that its partners need. In the meantime, the final policy should ensure a reliable power supply for national computing facilities and establish a technically skilled cadre within the government capable of operating and evaluating AI systems, supported by enforceable portability requirements in every public-sector technology contract.

At the UN General Assembly on September 24, Prime Minister Tarique Rahman said Bangladesh did not want today's digital divide to become tomorrow's AI divide. Closing that gap will depend less on which bloc Dhaka eventually joins than on whether it develops systems it can operate, modify and manage on its own terms. A country with that capacity can work with Washington and Beijing simultaneously and negotiate with both, while a country without it will find that its suppliers have effectively made the choice on its behalf.

Ayesha Tariq, CFA, is the CEO and co-founder of MacroVisor, an independent macro research firm.​
 

Experts for building AI-ready banking, fintech ecosystem
Staff Correspondent . Dhaka

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A panel discussion titled ‘Innovating for the Next Decade – AI, Digital Banks & the Future of Financial Services in Bangladesh’ is held as part of the 6th Bangladesh Fintech Summit in the capital on Saturday. | Press release photo

Banks and financial technology companies in Bangladesh need to embrace artificial intelligence, build practical AI skills, and strengthen collaboration to create a more efficient, secure, and inclusive digital financial ecosystem, experts said on Saturday.

They said that AI could improve banking operations, automate routine tasks, strengthen fraud detection and expand access to financial services.

Universities and financial institutions should also work together to prepare a workforce capable of applying the technology in practice, they added.

They made the remarks at a panel discussion titled ‘Innovating for the Next Decade – AI, Digital Banks & the Future of Financial Services in Bangladesh’ at the 6th Bangladesh Fintech Summit, organised by the Bangladesh Brand Forum in the capital.

Pubali Bank managing director and chief executive officer Mohammad Ali said that banks should integrate AI into account opening, identity verification, anti-money laundering compliance, loan processing and portfolio analysis.

AI-based systems could speed up account opening and help banks make faster decisions on personal, car and home loans, he said.

The technology could also help bank executives analyse corporate and small- and medium-enterprise portfolios and improve risk management, he added.

Mohammad Ali suggested setting up AI laboratories within individual departments to encourage practical applications across banking operations.

AI could eventually handle most routine, straight-through processing, leaving human employees to interpret complex cases and make decisions requiring judgement, he said.

He also called on universities to focus on practical AI applications in banking, including balance-sheet and cash-flow analysis, credit assessment, risk mitigation and regulatory compliance, rather than concentrating mainly on prototype development.

Shyamol B Das, managing director and chief executive officer of UCB Fintech Company Limited, which operates mobile financial service upay, said that banks and fintech companies should work together by combining fintech innovation with banks’ established services and customer trust.

He highlighted the potential of digital platforms and agent networks to reach underserved customers, while banks could provide credit and other financial products.

He said that better interoperability and cooperation among financial institutions could also help them use transaction histories to assess customers’ creditworthiness and expand financial inclusion.

Zakia Sultana Laboni, country manager of Mastercard Bangladesh, said that institutions should integrate AI into financial systems from the design stage to identify and prevent fraud and other risks, rather than responding only after problems occur.

She also stressed the importance of cybersecurity in digital banking, saying that security should be built into systems from the outset.

She called for continuous learning and stronger collaboration among technology companies, financial institutions and universities to develop AI-ready skills.

The speakers also highlighted embedded finance, digital wallets and agent networks as ways to make financial services more accessible, particularly to underserved customers.

They said that practical training and lifelong learning would be essential as AI reshaped banking and financial services.

In his welcome address, Sajid Mahbub, group chief executive officer and executive editor of the Bangladesh Brand Forum, said the next chapter of Bangladesh’s fintech journey would depend on how effectively the country turned digital adoption into lasting financial capability.

‘Innovation must make financial services more useful and accessible, while strengthening the trust people place in them. This requires institutions, entrepreneurs, and policymakers to work together on the infrastructure and standards that will support our financial future,’ he said.

Representatives from domestic and international banks, fintech companies, technology firms, financial institutions, and academia took part in three panel discussions, keynote sessions, and other sessions at the event.​
 

Can AI fix what cheap labour cannot in Bangladesh?

Mukhtar Ahmed

Bangladesh's garment industry is often explained by its low wages, yet research conducted inside its factories shows that wages do not explain why some firms perform far better than others. In a study of nearly 100 Bangladeshi factories, economists led by Christopher Woodruff and Rocco Macchiavello measured the efficiency of individual sewing lines. Lines at the 90th percentile were about 50 per cent more efficient than those at the 10th percentile, and the differences persisted over time, often between lines on the same factory floor. Capital investment accounted for only a small part of the gap.

Since the lines drew on the same labour market and paid broadly the same wages, labour costs could not account for it either. The researchers' wider work points to supervision and management as a large part of the answer.

I approach this evidence as an outsider. I am neither an economist nor a manufacturer; my work is in biotech, where I help decide which drug programmes deserve scarce capital and which should be stopped. That work consists largely of acting on incomplete evidence, and it has left me sceptical of a belief that accompanies almost every new technology: that a powerful tool will raise the performance of everyone who adopts it.

The belief that artificial intelligence will transform industries is now widespread. Bangladesh's garment industry, which earned $38.7 billion between July 2025 and June 2026 and accounted for about 80.6 per cent of the country's merchandise exports, employs around four million people, most of whom are women. AI may become widely accessible, but access alone does not guarantee economic returns. Firms with stronger management, better data and established processes appear better positioned to extract value from it. Whether AI ultimately widens or narrows productivity gaps remains an empirical question. Bangladesh still relies on cheap labour, but factory evidence indicates that labour costs alone cannot explain competitiveness.

Energy and finance come first, as the World Bank's Franziska Ohnsorge acknowledged this month when she urged governments to close the foundational gaps that hold back AI adoption.

What has changed

Most of what has changed this year has little to do with algorithms. In 2025, Vietnam overtook Bangladesh in apparel exports, shipping $39.6 billion worth of clothing against Bangladesh's $38.8 billion. Bangladesh's garment earnings fell by a further 1.6 per cent in the fiscal year ending in June.

The domestic environment has also deteriorated. In August, falling gas pressure left most factories around Dhaka operating at 60 to 70 per cent of capacity, according to BGMEA. Bangladesh is scheduled to leave the UN's list of least developed countries on 24 November, with implications for its preferential market access, although a request to defer graduation until 2029 remains before the General Assembly.

Buyers, meanwhile, want more information; the European Commission plans to adopt rules for a textile digital product passport in late 2027, requiring suppliers to provide structured data on materials and origins. AI adoption on Bangladeshi factory floors remains at an early stage and is largely vendor-led. Available international research cannot establish how widely Bangladesh's garment firms use AI or what returns they obtain.

The limits of the old advantage

Low wages built the industry, and they still help secure orders for basic clothing. The comparative evidence nonetheless indicates that cheap labour alone does not make an industry productive. Vietnam overtook Bangladesh while paying higher wages and selling a more diversified, higher-value product mix.

The most rigorous evidence comes from inside firms. In a randomised experiment published in 2013, Nicholas Bloom and colleagues provided free management consulting to Indian textile mills and found that productivity rose by 17 per cent within a year through better quality, higher efficiency and leaner inventories. The mills had not adopted these practices chiefly because they did not know about them, and badly run mills had survived because competition was too weak to force them out. Wages establish a baseline for competitiveness, while the organisation of production determines how far a firm can exceed it.

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Research identifies supervision and management as major factors behind productivity differences across sewing lines, even within the same factory. Photo: Mehhedi Hasan

Two technologies, two stories

Public debate in Dhaka often treats automation and AI as a single threat, though they operate differently and call for different responses. Automation can reduce the labour needed for particular tasks; its effect on total employment also depends on production volumes, new roles, wages, investment and market demand. A 2024 study by the Bangladesh Labour Foundation, BRAC University and Solidaridad reported reductions in labour requirements of about 31 per cent across the production processes studied, including 48 per cent in cutting and 37 per cent per line in sweater factories. These are findings from that study, not a measure of industry-wide job losses. Helpers, women and less-educated workers were particularly exposed.

Rubana Huq, whose Mohammadi Group runs automated sweater knitting operations, reported in 2018 that her factories had shed 500 jobs, and the Centre for Policy Dialogue estimated in July this year that up to 1.22 million garment jobs could disappear by 2041. Sewing, which employs most of the workforce, remains difficult to automate fully because robots still struggle to handle flexible fabric, and Miran Ali of BGMEA has said the industry will not fully embrace automation.

AI software affects workers indirectly by planning production, balancing lines, detecting defects and recording output. Oli Ahad, founder of Dhaka AI Lab, has argued that the industry has no choice but to adopt it, while trade unionist SM Ahasan Habib Bulbul has called for the gains from monitoring to be reinvested in workers. For most workers, the immediate questions are who sets the pace of work and who controls the data generated by their work.

Women are at the centre of both developments. Their share of the garment workforce fell to about 58 per cent by 2020, partly as automation eliminated helper roles, while at the start of the GEAR programme, 19 out of every 20 line supervisors were men. GEAR trained women operators to become supervisors, and an assessment by Oxford and BRAC University, published by the IFC and ILO in 2025, found that their lines were 2.5 to 4.3 percentage points more efficient than those of untrained supervisors, a gain worth up to $182,000 a year for a typical 20-line factory. A separate randomised trial found that women supervisors initially lagged behind their male counterparts, largely because workers doubted their abilities, but matched men's performance within four to six months. Automation pushes women out at the bottom of the factory hierarchy, while management practices keep them out of supervisory roles; the latter problem reduces output and is less costly to address.

Why organisation matters for AI

The World Bank's World Development Report, published in August, includes a finding that should shape this debate. Its supporting research distinguishes access to technology from the intensity of its use. Direct evidence from a survey of Romanian firms shows that AI adoption is concentrated among larger, more productive and better-managed firms. The researchers caution that these findings do not establish causal effects or represent all developing countries. A study of British firms by Cambridge's Bennett Institute reinforces the point. Firms with better management practices in 2020 were significantly more likely to have adopted AI by 2023, yet the same practices did not predict the adoption of robots or specialised equipment. These findings identify an advantage in adoption; they do not establish which firms will capture most of the eventual productivity gains.

The probable reason is practical. AI tools used for planning and quality control depend on reliable operational data and on the decisions taken in response to their recommendations. My own field offers a parallel. In drug discovery, computational tools can generate predictions and rank opportunities. Their commercial value depends on the quality of the evidence, the experimental systems used to validate those predictions and the organisation's willingness to discontinue unsuccessful projects. In portfolio management, a promising prediction is a reason to design the next experiment, not sufficient grounds to commit more capital. The software does not supply that judgement.

Buyers are moving more quickly than factories. Shein, the online retailer, makes 100 to 200 pieces of each new style, reorders only what sells and runs its suppliers on its own software. In the Oxford data, lines producing for higher-end buyers were more efficient even within the same factory. In July, Ha-Meem Group signed an agreement with the robotics company Robonauts that lists workforce development ahead of automation, which is the sequence this evidence recommends.

The strongest objection

A factory owner in Gazipur could reasonably object that this inverts the order of problems. Supervisor training improves line efficiency by a few percentage points, whereas this year's gas shortages have cut capacity by a third or more across much of the industry. Losing least-developed-country status could add tariffs of around 9 per cent in Europe and the UK on key products, according to one think tank's estimate, while credit remains scarce and costly. Miran Ali made a version of this argument in 2024, blaming power cuts and port and road congestion for much of the industry's low efficiency. The owner might add that buyers capture the gains. Mark Anner found that the prices American importers paid for Bangladeshi trousers fell by about 41 per cent between 2000 and 2014, even as lead times shortened, and AI may concentrate demand data further in buyers' hands.

AI software affects workers indirectly by planning production, balancing lines, detecting defects and recording output.
Much of this is correct, and my argument must acknowledge it. Energy and finance come first, as the World Bank's Franziska Ohnsorge acknowledged this month when she urged governments to close the foundational gaps that hold back AI adoption. The objection nonetheless underestimates how shocks operate. Bloom's mills showed that badly run firms survive when pressure is low, and the next three years will not be such a period. Even August's crisis prompted an organisational response, with BGMEA reporting that factories ran dyeing units during off-peak hours to build fabric stocks while continuing to pay workers. Some managed this; others did not. Capability is also a source of bargaining power, since a factory that can supply small batches, traceable data and consistent quality can choose buyers who tend to pay more. The argument therefore survives in a narrower form. Energy, finance and market access will determine whether the industry retains its scale; organisational capability may help explain which firms within it adapt successfully. AI could raise the cost of lacking that capability, but whether it also helps firms acquire it remains to be tested.

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Public debate in Bangladesh often treats automation and AI as a single threat, although they operate differently and require different responses.

What would prove this wrong

There is a serious possibility that AI could help weaker firms catch up. In the best-known field study of generative AI at work, Erik Brynjolfsson, Danielle Li and Lindsey Raymond found that an AI assistant raised the productivity of more than 5,000 customer-support agents by 14 per cent on average, with novices becoming about a third more productive and the most experienced workers gaining almost nothing. The tool appeared to make experienced workers' practices more accessible to newcomers. Call centre evidence cannot be transferred directly to garment manufacturing, but it raises a question: could inexpensive planning and inspection tools give weaker factories access to capabilities they currently lack? If those factories achieve larger, sustained efficiency gains, AI would be reducing an existing management disadvantage. Over the next three to five years, researchers should compare changes in efficiency among factories adopting AI with different levels of initial capability, accounting for changes in orders, product mix and other investments. A narrowing gap would challenge the hypothesis that AI mainly compounds existing advantages. Researchers such as Atonu Rabbani at the University of Dhaka and Christopher Woodruff at Oxford have worked with the line-level data needed to investigate this, provided factories and buyers are willing to share the relevant figures.

What follows

For factory owners, the sequence of investment matters more than its scale. Measurement should come first, because a firm that knows its standard minutes and defect rates can judge whether an AI tool helps, while one that does not must accept the vendor's figures. Investment in supervisors, including women, should follow, since the evidence supporting it is stronger than that for any software. Treat each AI product as a trial with a defined measure of success and a stop date, as disciplined drug developers do with their own programmes.

For the government, energy and finance remain the first priorities, and no technology policy can substitute for them. A cheaper step is measurement. A national survey using the World Bank's enterprise module on AI would show which firms are falling behind before the market does. Buyers, for their part, will obtain traceable, small-batch supplies sooner from suppliers who share in the gains.

The lesson extends beyond Bangladesh. For a decade, economists have asked whether automation will close the ladder of labour-intensive manufacturing that Korea, China and Bangladesh climbed. The evidence from Bangladesh's factory floors suggests another risk: that organisational weakness could limit firms' ability to benefit even while cheap labour continues to win orders. AI might deepen that disadvantage or help firms overcome it. The next test of Bangladesh's industrial competitiveness may be less about which factories acquire AI first than which can turn technology into measurable improvements in productivity, quality and reliability. The distinction will matter long after today's technologies have changed.

Mukhtar Ahmed, PhD, is a biotech executive and strategic adviser.​
 

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