Roshan Bhandari
Team Lead · Software Engineer · DevOps
Available for Projects
An honest examination of whether Nepal can build a genuine position in AI — the real advantages, the structural barriers, and the specific bets that could make it happen.
A decade ago, asking whether Nepal could become a serious player in global software development would have seemed optimistic to the point of naivety. Today, Nepali engineers are building infrastructure at Amazon, running engineering teams at European startups, and shipping products used by millions of people who have never heard of Kathmandu. The software development story happened faster than most observers predicted.
The AI question deserves the same honest examination — neither the uncritical optimism that treats every emerging technology as Nepal's next breakthrough, nor the reflexive scepticism that assumes developing countries can only participate in global technology as consumers and low-cost labour.
The real answer is more specific than a yes or a no. Nepal is unlikely to compete with the US, China, or the UK in foundational AI research requiring billions in compute. It could, with the right choices, build a meaningful position in applied AI, domain-specific AI for underserved markets, and AI-driven services that leverage its existing talent base. Whether it does depends on decisions being made right now — by developers, by companies, by universities, and by the government.
Nepal is not starting from zero. Fusemachines has spent years building the argument — with evidence — that Nepali talent can be developed for serious AI work. Their AI Microdegree programs, run in partnership with Nepali universities, have trained thousands of engineers in machine learning fundamentals. Sameer Maskey, the company's founder, built a career at Columbia University's AI lab before returning attention to Nepal. The pipeline is not imaginary.
CloudFactory built one of the world's most operationally sophisticated AI data businesses and staffed a significant portion of it from Nepal. The lesson from CloudFactory is not just that Nepali workers can do AI-adjacent work — it is that Nepali operations can meet the quality standards that demanding AI companies require at scale.
Beyond these anchors, Nepal produces thousands of engineering graduates annually from institutions like Pulchowk Campus (IOE) and Kathmandu University. The graduates entering the workforce now are doing so into a global environment where AI is a baseline literacy, not a specialisation — and a meaningful number are developing genuine AI skills alongside their traditional software engineering education.
Nepal's diaspora in North American and European technology hubs is larger and more senior than its domestic profile suggests. Nepali engineers, researchers, and product leaders working at Google, Meta, Microsoft, Amazon, OpenAI, and research universities represent a knowledge network that few countries of Nepal's size possess. The question is how much of that network connects back to Nepal — through investment, through mentorship, through returning to build.
The strongest precedents for diaspora-driven tech ecosystems — Israel, India, Taiwan — all required deliberate cultivation: policies that made it attractive to return or invest from abroad, institutions that created reasons to engage. Nepal has the raw material of diaspora connection. Converting it into institutional AI capacity is a strategic question, not a technical one.
AI leadership does not require being first in all AI. It can mean being best in specific domains where you have genuine informational and contextual advantages. Nepal has several:
Environmental and climate AI: Nepal sits at one of the world's most sensitive climate intersections — glacial melt in the Himalayas, changing monsoon patterns, biodiversity under pressure, disaster risk from floods and landslides. The data infrastructure for environmental monitoring is growing, and the scientific community's interest in Nepal as a climate observation point is real. AI applied to these challenges — glacier monitoring, flood prediction, agricultural climate adaptation — is both technically interesting and globally relevant. Research institutions that build here build with unique data.
Low-resource language NLP: Nepali is spoken by roughly 17 million people. Maithili, Bhojpuri, Newari, and dozens of other languages are spoken across Nepal by populations whose linguistic needs are almost completely unserved by current AI systems. Building language technology for low-resource South Asian languages is a research problem that elite institutions have engaged with but not solved — and it is a problem where proximity, cultural context, and access to native speakers gives Nepali researchers a genuine edge.
Healthcare and agricultural AI for the Global South: Nepal's health and agricultural challenges mirror those of dozens of countries across South Asia, Sub-Saharan Africa, and Southeast Asia. AI solutions built and validated in Nepal's context — low infrastructure, limited data, high linguistic diversity — have immediate applicability across a billion-person market that Silicon Valley tools do not serve well.
Building an AI research or product organisation costs dramatically less in Nepal than in San Francisco, London, or even Bangalore. A team of five exceptional ML engineers in Kathmandu costs what one engineer costs in a US AI company. This is not a permanent structural advantage — costs rise as the ecosystem matures — but it is a meaningful window during which Nepal-based organisations can iterate at a speed and cost efficiency that competitors in expensive markets cannot match.
For AI startups in particular, where iteration speed matters more than almost anything else, the ability to run more experiments with less capital is a real competitive advantage.
Modern AI — specifically the large language models and foundation model research that defines the frontier — requires extraordinary amounts of computing power. Training a foundation model costs millions of dollars in GPU time. Fine-tuning large models requires cloud infrastructure at a scale that is expensive anywhere and made more expensive in Nepal by unreliable power and limited data centre infrastructure.
This is not an insurmountable barrier for all AI work — most applied AI, inference, fine-tuning of smaller models, and RAG-based applications run on infrastructure that is globally accessible through cloud providers. But frontier model research is effectively closed to organisations that cannot afford the compute, which rules out truly cutting-edge foundational research from Nepal without external partnership or funding.
The honest implication: Nepal's AI innovation will be in the application layer and in domain-specific models, not in the next GPT or Gemini. That is not a consolation prize — application-layer innovation is where most of the economic value in AI is being created — but it is a realistic constraint on scope.
Nepal's most talented engineers and researchers face a structural incentive to leave. An ML engineer who can earn $150,000–$300,000 at a US AI company earns a fraction of that domestically, even with a significant premium for AI skills. The Nepali developers who have reached the frontier level of AI capability are globally mobile — and most of them are in the US, Europe, or Singapore.
This is not a reason for despair but a reason to be realistic about what keeps talent in Nepal or connected to Nepal. The strongest retention mechanisms are not salary matching — Nepal cannot match Silicon Valley compensation and probably should not try — but quality-of-life factors (family, cost of living, meaningful work), equity in Nepal-based ventures, and the specific appeal of working on problems that only exist here.
Nepal does not yet have a coherent national AI strategy. Other countries at comparable development stages — Rwanda, Estonia, Kenya — have moved deliberately to create policy frameworks that attract AI investment, establish data governance rules, fund AI research at universities, and position the country in global AI conversations. Nepal's technology policy has historically been reactive rather than proactive.
Data infrastructure is a related gap. Quality AI requires quality data. Nepal's public data — health records, agricultural yields, transportation patterns, environmental measurements — exists in fragmented, often paper-based, often inaccessible forms. Building the data infrastructure that makes AI systems trainable is a prerequisite for the AI ecosystem to develop.
AI ecosystems are self-reinforcing: talent attracts companies, companies fund research, research produces graduates, graduates attract more companies. Kathmandu does not yet have the density of AI-focused companies, labs, and research groups to create this loop on its own. The ecosystem is early enough that individual organisations — a Fusemachines, a well-funded university program, a returning diaspora researcher — can disproportionately accelerate or retard its development.
This is a risk and an opportunity simultaneously. It means the ecosystem is fragile — dependent on a small number of actors making the right decisions. It also means that individual contributions matter more than they would in a mature ecosystem. A single excellent ML research group at Tribhuvan University or Pulchowk Campus could reshape what is possible.
Leadership in AI does not have one shape. The US leads in foundation model research and private capital deployment. China leads in AI application at scale and government coordination. Israel leads in AI defence and cybersecurity applications. Estonia leads in AI governance and digital identity. Each model reflects different national strengths and choices.
The most credible version of Nepali AI leadership is not trying to compete with OpenAI. It is becoming the global reference point for specific things:
No other country will prioritise building AI for the specific environmental, ecological, and disaster management challenges of the Himalayan region. If Nepal builds the leading research and application capability in this domain, it becomes the world's reference for it by default. This has happened in other domains — Switzerland for financial infrastructure, the Netherlands for water management, Kenya for mobile money — where concentrated expertise in a specific context creates global influence.
Building the best open language models for Nepali, Maithili, and the other languages of the Himalayan arc — and the research tools and datasets that enable this — is a concrete, achievable goal with global relevance. The Masakhane project has demonstrated what a community-driven approach to African language NLP can accomplish from the African continent itself. A Nepal-led equivalent for South Asian languages would have both academic significance and direct commercial applications.
As Indian, Bangladeshi, Sri Lankan, and Pakistani businesses adopt AI, they will need customisation, integration, and implementation services that understand the South Asian business context — payment systems, regulatory environments, language requirements, user behaviour. Nepal, positioned at the intersection of South Asian markets and a growing pool of AI-skilled engineers, could build a regional AI services industry comparable to what India built in IT services in the 1990s and 2000s.
The engineering curricula at Nepal's universities are improving but have not kept pace with where AI engineering is going. ML courses taught with 2015 frameworks, no access to GPU compute for training, no industry-connected faculty — these are solvable problems that require deliberate institutional investment, not a decade of gradual reform. Universities that move quickly on AI curriculum, research infrastructure, and industry partnerships will produce the graduates that companies locate near.
The government sitting on unstandardised, inaccessible datasets is not neutral — it actively slows the AI ecosystem. Health data, agricultural yield data, satellite imagery, census data — made available in machine-readable formats with appropriate privacy protections — would enable a wave of AI applications that are currently impossible. This is a policy decision, not a technical one, and it costs less than most government technology investments.
Anecdotal diaspora engagement — individuals returning for a conference, making a donation to their alma mater — produces anecdotal results. Structured programs — equity frameworks for diaspora investors in Nepal-registered companies, fast-track visa options for returning talent, university-industry joint ventures with diaspora-led companies — produce structural outcomes. The countries that have successfully leveraged diaspora for technology ecosystem development (Israel's Yozma program, India's liberalisation of diaspora investment) did it through deliberate design, not goodwill alone.
Institutions and policy matter, but ecosystems are built from the bottom up by individuals making choices. Every Nepali developer who spends the next twelve months building genuine AI skills — not collecting certificates but shipping AI products, contributing to AI research, building AI-enabled tools — is a unit of ecosystem development.
The developers who chose to learn cloud infrastructure five years ago are running DevOps teams today. The developers who chose to specialise in mobile development when smartphones arrived are leading mobile practices today. The developers who invest in AI depth now are positioning themselves and, collectively, the Nepali tech ecosystem for the decade ahead.
Can Nepal become a leader in AI innovation? Yes — in the specific, meaningful sense of building the world's best capability in domains where Nepal has unique advantages. Not in the generic, chest-thumping sense of competing with the US or China for frontier model supremacy.
The Himalayan environment, the South Asian language gap, the cost structure for applied AI development, the existing talent pipeline from Fusemachines and Pulchowk, the diaspora network with seniority at major AI organisations — these are real assets. They do not automatically convert into AI leadership. They require choices: by developers building skills, by companies building products, by universities building research programs, and by a government that decides AI is worth a coherent national policy rather than a mention in a budget speech.
The window is open. Whether Nepal walks through it is not a question about technology. It is a question about ambition and institutional will. Both are in shorter supply than talent, and both are more important than raw ability at this stage.
The talent exists. The question is whether it will be organised well enough and supported clearly enough to produce something the world notices.