Is AI exposing a skills gap Bangladesh can no longer ignore?

Artificial intelligence is changing the way we work, learn, and solve problems. Tasks that once required specialized knowledge, substantial resources, or weeks of effort can now be completed much faster with tools such as ChatGPT, Claude, and AI-assisted development platforms. A software engineer can build an application in a fraction of the time previously required. An entrepreneur can explore business ideas, analyze markets, and develop prototypes without assembling a large team.

For Bangladesh, the opportunity is substantial. Our technology workforce and software services industry could use AI to compete for more sophisticated work and build stronger products.

Yet through my work with technology professionals in Bangladesh, I have noticed something worth examining. People using the same AI tools often achieve very different results. Some define problems clearly, question responses, and improve the outcome. Others accept recommendations without fully understanding them.

Technical knowledge and experience certainly matter. But I have increasingly observed that people with stronger English comprehension, reading comprehension, logical reasoning, and critical thinking skills often seem better equipped to work effectively with AI. This is a professional observation, not a scientific conclusion. Nevertheless, it raises a question worth examining: are we concentrating too heavily on access to AI while paying insufficient attention to the intellectual skills needed to use it well?

Access does not guarantee capability

Discussion of AI adoption often centers on infrastructure, computing resources, and training. These matter, but access and capability are different.

Anyone can ask an AI system to write software, prepare a financial analysis, or recommend a business strategy. The result may be polished, detailed, and convincing. The more difficult questions come afterward. Does the user understand the assumptions? Can they identify missing information? Do they recognize when a proposed solution is inappropriate for the situation? Can they explain why they would accept or reject it?

AI is making sophisticated work easier to produce, but not necessarily easier to understand. That distinction matters when the output informs financial decisions, security, or public services.

AI readiness should therefore mean more than knowing how to operate an application. It should include the ability to assess its work.

Does English provide an advantage?

Bangla is our mother tongue, and language proficiency should never be confused with intelligence. Someone who reasons effectively in Bangla is not inherently less capable than someone fluent in English. We should be particularly careful not to turn a discussion about AI into an argument that privileges one educational background over another.

Still, the technology ecosystem offers practical advantages to people comfortable with English. Much technical documentation, scientific research, and programming material is published in English. Large language models have also been trained on substantial English-language material, although their multilingual capabilities are improving.

Research suggests that language can affect model performance. The 2024 study BEnQA: A Question Answering Benchmark for Bengali and English evaluated approximately 5,000 parallel Bengali and English questions on educational subjects relevant to Bangladesh. Researchers found differences across languages and benefits from English translations in certain settings.

Another 2024 study, BenLLM-Eval, examined several large language models on Bengali language tasks and identified limitations in their performance. These findings do not establish that English always produces better answers. Results depend on the model, the task, the evaluation method, and the cultural context. Bangla may be preferable for certain local questions, and multilingual AI is developing rapidly.

Nevertheless, language can influence AI responses. English comprehension may provide a practical advantage for Bangladeshi technical professionals, though it is only one factor.

Reading comprehension may matter even more

There is an important difference between being able to read something and understanding what it means. A person may recognize most of the words in a technical explanation yet miss a qualification, assumption, or consequence. This distinction becomes especially important when AI produces long, confident responses.

Consider a software engineer using an AI assistant to develop an application. The AI reports that the implementation is complete and all existing tests have passed. In the same response, it mentions potential security vulnerabilities or scenarios that remain untested. One engineer may interpret the report as confirmation that the application is ready for deployment. Another recognizes that passing tests does not eliminate the unresolved risks and investigates further.

They received the same information but reached different decisions. The difference was not access to AI. It was comprehension and judgment.

Similar problems arise when a financial analyst reviews an AI-generated forecast or a manager evaluates a business recommendation. A polished response is not necessarily a sound one.

AI makes information easier to obtain. It does not remove the need to read carefully, in English or Bangla.

Critical thinking becomes more valuable, not less

Modern AI systems can produce remarkably convincing responses even when their conclusions are incorrect. An explanation may be well structured and technically sophisticated while containing flawed assumptions. The ability to recognize this is not a minor skill. It is central to using AI responsibly.

Effective users do not simply accept an answer because it sounds reasonable. They ask whether the information is sufficient, whether an alternative explanation exists, and what evidence would confirm or disprove the recommendation. They understand that an AI system can be helpful without being authoritative.

An AI assistant may propose software architecture suitable for a demonstration but unsuitable for thousands of users. An experienced engineer may spot scalability, security, or reliability risks that another user misses.

Likewise, business leaders need to challenge the assumptions behind AI-generated forecasts, not simply admire the presentation.

I am not arguing that only experts should use AI. One of the technology's greatest benefits is that it helps people learn and perform tasks beyond their previous experience. My point is that we should not confuse a convincing answer with a verified one. AI can help us accomplish more, but it does not automatically make our decisions better.

The AI verification gap

AI is increasing our ability to produce sophisticated work. Our ability to verify it may not be improving as quickly. I think of this as the AI verification gap.

A developer can generate thousands of lines of code quickly, but may not understand the architecture well enough to identify vulnerabilities or failures. A business professional can produce a market assessment in minutes, but may not know whether its sources and assumptions are reliable.

The concern is not that AI necessarily produces poor work. In many situations, it produces excellent work and saves substantial time. The concern is that the apparent sophistication of the output may exceed the user's ability to understand and evaluate it. A polished presentation can conceal weak analysis. Working software can conceal serious security flaws. An articulate explanation can conceal factual errors.

I use AI verification gap as a framework for discussing this risk, not as an established scientific measure.

As businesses become more dependent on AI-generated work, they need processes and people capable of meaningful oversight. The answer is not to reject AI or insist on checking every trivial output manually. It is to develop the judgment to know what must be verified, how rigorously, and by whom.

AI can narrow some skill gaps while exposing others

AI does not benefit only advanced professionals. In Generative AI at Work, Erik Brynjolfsson, Danielle Li, and Lindsey Raymond studied more than 5,000 customer support agents. They found average productivity gains of approximately 14 percent, with particularly large gains among less experienced and lower-skilled workers.

That is encouraging. AI can help people overcome gaps in knowledge and experience.

However, another possibility deserves consideration. AI may reduce differences in routine execution while leaving significant differences in complex problem-solving, comprehension, and judgment.

Someone with limited experience may become substantially more productive using AI. But when the system misunderstands a requirement or produces a flawed recommendation, recognizing the problem may still depend on human reasoning and expertise.

Both effects can exist simultaneously. AI can narrow certain skill gaps while making other capabilities more important. We should investigate this possibility rather than assume that AI will either eliminate inequality or inevitably widen it. The outcome is likely to depend on the work being performed, the support available, and the skills of the person using the system.

What this means for Bangladesh

Bangladesh could use AI to strengthen software exports, entrepreneurship, and public services. But training people to operate AI applications is not enough.

Concerns have long been raised about memorization and examination performance in parts of our education system. Are students learning to interpret complex material, distinguish evidence from opinion, explain their reasoning, and challenge assumptions?

Bangladesh has outstanding engineers, students, and researchers. The question is whether we develop these abilities consistently and at scale.

Different educational pathways offer different exposure to English-language resources. We should study the effects without assuming that English-medium students are inherently better thinkers. Clear reasoning matters in both Bangla and English.

This is particularly important if Bangladesh wants to move beyond competing mainly on the cost of software services. Higher-value engineering and product development require the ability to understand customers' problems, make architectural decisions, manage risk, and create intellectual property. AI may accelerate the technical work, but these responsibilities still demand capable people.

The strategic question is not only how much faster we can write code, but whether we can design, validate, and own more sophisticated solutions.

Rethinking how we develop and evaluate talent

AI is changing how employers evaluate talent. Programming knowledge still matters, but routine code generation is easier. Understanding requirements, evaluating architecture, and identifying risks remain difficult.

Recruitment could include reviewing an AI-generated solution with hidden weaknesses. Can candidates identify the problems, explain their consequences, and propose better approaches?

Similar exercises could assess analysts and managers. The ability to question an AI-generated report may reveal more than the ability to produce one.

This is not an argument against technical proficiency. Rather, technical knowledge and reasoning should reinforce each other. The goal should be to develop professionals who can direct AI effectively, evaluate its work, and accept responsibility for the outcome, rather than people who are merely proficient at prompting a system.

Better Bangla AI and Bangladesh-specific research

English comprehension matters for international technical work, but Bangladesh must also invest in stronger Bangla AI. High-quality datasets, evaluation benchmarks, and applications reflecting local institutions and terminology are essential.

People should not need exceptional English proficiency to benefit from advanced AI. Better Bangla systems can broaden access while English skills support global collaboration.

We also need local research. A study could assess participants’ reading comprehension, English proficiency, reasoning, and critical thinking, then measure their performance on comparable tasks using the same AI tools. It could compare Bangla and English interaction and test whether training improves outcomes.

Such research should account for educational background, technical experience, and prior exposure to AI. Simply observing that people with stronger English skills perform better would not establish that

English caused the difference. Controlled experiments and well-designed training studies would help us understand what actually improves performance.

Findings could inform curricula, recruitment, training, and AI policy. My professional observations are a reason to investigate, not a substitute for evidence.

Preparing Bangladesh for the AI economy

Bangladesh needs AI infrastructure, research, technical education, and adoption. We also need to strengthen the human capabilities that make those investments useful.

Reading comprehension, analytical reasoning, critical thinking, and effective communication should receive greater attention throughout education and professional training. Universities, employers, and policymakers should work together to understand which skills improve AI-assisted performance and how those skills can be developed. We should build better AI in Bangla while ensuring that students and professionals can engage with the international knowledge base.

AI makes it easier to obtain information, generate software, and produce analysis. Responsibility for understanding problems and evaluating decisions remains with people.

For Bangladesh, this is both an educational and economic competitiveness issue. Higher-value innovation requires professionals who do more than operate intelligent tools.

AI makes answers easier to produce. Our success will depend increasingly on whether we understand those answers, know when to question them, and recognize when they are wrong. The quality of our human thinking may matter as much as the sophistication of the technology we adopt.