Dr. Muhammad Amir Malik (S.I.)
How prize competitions can turn public problems into companies, skills, and lasting national capability
Turn a national need into an invitation to build
Consider a government seeking better flood warnings. A challenge could invite teams to improve accuracy, reach remote communities, and help people act sooner. Alongside a better service, the country could gain a new business, experienced engineers, and an overseas team willing to establish local operations.
That is the case for treating AI challenges as instruments of national development. A challenge sets a problem and invites competing approaches, backed by prizes, development funding, or access to customers and facilities. Its rules can connect a pressing need with several longer term goals.
Countries with limited AI budgets have a particular reason to consider this approach. They can concentrate effort on problems where local demand and expertise give them an advantage, while inviting ideas from anywhere. The winning solution is one return on a challenge. The larger opportunity is to strengthen the country’s ability to adopt AI, build companies, develop talent, and solve its next problem.
This distinction matters more than it first appears. A procurement exercise buys a product. A challenge buys a product and, at the same time, trains the workforce that will maintain it, exposes the data gaps that would have sunk a conventional project, and signals to investors that a market exists. When designed well, the competition is not an event appended to a national AI strategy. It is the strategy made visible: a public, deadline driven act of nation building in which the government defines the problem, the market supplies the ingenuity, and the country keeps the gains.
A focused problem can produce several kinds of value
Accelerate adoption through real work
A challenge gives an AI strategy a starting point. A hospital, municipality, or industry group identifies a problem. Teams gain access to users, a deadline, and a test of whether their solution helps. This focuses effort and surfaces obstacles early, including data gaps or process changes.
Singapore’s Government Technology Agency, GovTech, illustrates the approach through its {build} programme. Staff propose problems and form cross disciplinary teams. A six week sprint combines research, prototyping, and testing with users and problem owners. The useful acceleration comes from learning sooner what works and what blocks adoption. A challenge can organise that work across agencies, universities, and businesses, giving each a defined contribution to a shared result.
The deeper value is institutional memory. Most failed digital government projects fail in the same quiet ways: the data was not where anyone thought it was, the frontline staff were never consulted, the procured system could not survive contact with a real workflow. A challenge compresses these lessons into weeks rather than years, and it does so in public, where the lessons become common property. Every agency that watches a cohort of teams struggle with the same missing dataset learns something no consultant’s report could teach it. Adoption accelerates not because the technology improves, but because the institutions around it finally understand what adoption requires.
Give startups demand they can build a business around
A well designed challenge makes a problem worth an entrepreneur’s time. It reveals a prospective customer, provides access to the people who understand the work, and can help finance development. Those conditions give a young firm a reason to pursue an opportunity it might otherwise leave alone.
The commercial opportunity should extend beyond the sponsor. Consider a hypothetical challenge to detect equipment faults in smaller factories. A solution developed with participating manufacturers could serve other factories and export markets. Allowing firms to retain reusable intellectual property helps make that possible.
This can strengthen the startup ecosystem beyond the winner. Finalists may gain customer knowledge, partners, or investor interest. Simple entry rules and workable payment terms determine whether promising newcomers can participate in the first place.
The economics here deserve emphasis. Early stage technology firms rarely die from bad ideas; they die from the absence of a first credible customer. Government is the largest buyer in nearly every economy, yet its procurement rules typically filter for exactly the firms a startup is not: large, established, and able to wait months for payment. A challenge inverts this. It advertises demand before the product exists, lets a two person team compete on merit against incumbents and, when payment is tied to accepted milestones rather than purchase orders, respects the cash flow reality of small companies. Done repeatedly, challenges convert the state from an inaccessible fortress of demand into the anchor customer around which an entire generation of firms can be built.
Build capability that survives the competition
The country should keep useful skills and resources from the work it supports. Local engineers can learn to build and test systems; public employees can learn to judge them. Shared software and evaluation tools allow others to continue the work.
The US Defense Advanced Research Projects Agency (DARPA) demonstrated this wider return through its AI Cyber Challenge. In the 2025 final, competitors found 54 deliberately introduced software vulnerabilities and patched 43. They also found 18 real vulnerabilities that organisers had not inserted. DARPA’s results announcement reported that four finalists had already released their systems as open source software, allowing others to study and improve them.
These were competition results, not proof of reliability in every operating environment. But the released tools created a starting point for further development. Governments can seek similar returns through shared test resources, research partnerships, and local participation in technically demanding work.
The lesson extends well beyond cybersecurity. Every challenge generates three assets that outlive the prize ceremony: trained people, tested tools, and trusted evaluation methods. A government that lets these dissipate has purchased a trophy. A government that captures them, through open source requirements, shared benchmarks, secondments of public employees into competing teams, and publication of evaluation frameworks, has purchased an industry. The decisive design question is therefore not only who wins, but what the country is left holding when the cameras leave.
Bring promising ideas and their builders into the country
An international challenge can serve as a search for talent and businesses. It reaches teams beyond the government’s existing contacts and lets them demonstrate what they can do. A strong offer then gives selected teams a reason to build locally: customers, research facilities, collaborators, and access to a wider market.
Attracting those teams requires practical help with business setup, including incorporation and immigration support. Relocation can be one outcome; an engineering centre, local venture, or substantial research partnership can also create value. The goal is sustained activity. Connecting incoming teams with domestic firms and universities gives local entrepreneurs opportunities to learn, recruit, and collaborate. The country gains when international participation becomes part of its own productive capacity.
There is a quiet strategic insight here that many governments miss. Traditional investment promotion tries to persuade companies to come and then hopes they succeed. An international challenge reverses the sequence: it lets firms prove themselves first, against a real local problem, before anyone commits a visa, an office, or a grant. The selection risk falls dramatically because performance replaces presentation. And because the foreign team has already built something for a local customer, its incentive to stay is commercial rather than cosmetic. The challenge becomes not merely a magnet for talent but a filter for commitment.
Encourage solutions that can adapt
Challenges let governments define the result while leaving room for unexpected approaches. The tests should reward performance under the conditions a country expects to face, including changing data, local languages, and unreliable connectivity.
DARPA’s cyber competition made that discipline concrete. Its scoring rules gave repairing vulnerabilities three times the weight of identifying them, and required repairs to preserve software functionality. Teams had an incentive to produce a usable result.
For future AI challenges, sponsors should test whether a solution can accommodate better models, new information, and changing needs. They can also use the work to identify missing standards or unclear regulations. The country learns what it must improve around the technology to make adoption easier.
Adaptability is the most undervalued criterion in public technology buying, and the one challenges are uniquely placed to test. Artificial intelligence is the fastest moving general purpose technology in history; a system specified in January may be built on a model that is obsolete by December. Contracts written around fixed specifications lock governments into yesterday’s tools. Challenges written around outcomes, tested against degraded connectivity, shifting data distributions, and languages that global vendors ignore, force builders to demonstrate resilience rather than promise it. The governments that learn to score for adaptability will own systems that improve with time. The rest will inherit expensive monuments to the state of the art as it once was.
Four countries, four lessons, one model
The UK opens public demand to young firms
The UK’s £100 million competition, funded across the scheme’s lifetime, links public service priorities to the growth of British AI firms. Its first four challenges cover health service productivity, computing efficiency, defence applications, and the security of AI agents, systems that carry out tasks using software tools. Those choices create opportunities beyond a single contract. More efficient computing and better security could support AI adoption across many sectors.
The guidance removes minimum turnover and trading history requirements and lets suppliers retain their intellectual property, with public sector usage rights for government. Payment normally follows accepted milestones; advances for cash constrained micro and small firms require approval. The design connects three things: a public need, access for young firms, and room to develop a wider business. As of mid 2026, the scheme is too new to establish results. Its significance lies in the explicit link between public buying and industrial development.
What deserves particular attention is the philosophy embedded in the fine print. By striking out turnover thresholds and trading history tests, Britain acknowledged that its procurement rules had been selecting for incumbency rather than capability. By letting suppliers keep their intellectual property, it accepted that the state’s interest is not ownership but diffusion: a firm that owns its invention can sell it to the world, pay taxes at home, and hire British engineers. The UK’s wager is that the cheapest way to grow an AI industry is not subsidy but access, and that a government confident enough to be a demanding first customer will be repaid in companies rather than merely in software.
Singapore turns a competition into a magnet for customers and capital
Singapore’s 2026 Global FinTech Hackcelerator organises competition around problems supplied by financial institutions. GXS Bank focuses on credit and fraud risk, Julius Baer on digitally native wealth clients, and Zurich Insurance on helping smaller businesses understand their risks.
The Monetary Authority of Singapore (MAS) and the Global Finance and Technology Network offer up to 20 finalist places, with S$80,000 for each problem’s winner. The wider programme includes a S$20,000 participation stipend, mentorship, investor meetings, and industry connections. Applicants need not already be based in Singapore. The offer combines business demand with access to a financial centre. It gives founders reasons to participate beyond prize money and positions Singapore as a place to develop customer and investor relationships.
The 2026 competition has not concluded as of this writing, and any subsequent pilot depends on mutual agreement. The lesson is in how Singapore assembles an attractive opportunity around the challenge.
Singapore’s genius is compositional. The prize money is almost incidental; the real prize is proximity to the people who can write the next cheque. By sourcing problem statements from banks and insurers rather than from ministries, Singapore guarantees that every finalist is solving a problem someone with a budget actually has. By opening applications globally, it treats its own market not as a protected garden but as a showroom: come, compete, meet the customers, meet the capital, and then decide where to plant your company. Few governments have understood as clearly that in a mobile world, a challenge is above all an act of economic diplomacy.
South Korea converts international discovery into local industry
South Korea’s K-Startup Grand Challenge explicitly seeks overseas entrepreneurs. In 2025, 2,626 teams from 97 countries applied. It selected 40 teams for support with market entry, visas, registration, and settlement. The top 20 qualified for further support, including business connections and workspace.
The programme spans sectors rather than focusing only on AI. One relevant participant is Polymerize, which uses AI in materials development. Korea’s startup ministry reported in March 2026 that it had created 11 local jobs after entering Korea in September 2024. The company received support through both the challenge and a separate commercialisation programme, so those jobs cannot be attributed to the competition alone. The combination matters. A competition attracts and selects teams; business support helps them establish operations. Korea’s example shows how an international invitation can connect to the practical work of bringing productive activity into the country.
Korea’s contribution to the playbook is administrative honesty. It recognised that foreign founders do not fail abroad for lack of brilliance; they fail on visas, leases, incorporation paperwork, and the thousand small frictions of an unfamiliar system. So it built a conveyor belt from selection to settlement, and it measures success in jobs created on Korean soil rather than in applications received. Eleven jobs from one materials startup may sound modest. It is, in fact, the point: national advantage compounds quietly, one payroll at a time, and the countries that engineer the compounding are the ones that collect it.
Pakistan turns national necessity into national strategy
Pakistan offers the newest and, in several respects, the most instructive case, because it has arrived at the challenge model not as an experiment but as a national necessity. Few countries need better flood warnings more urgently. The 2022 floods submerged roughly a third of the country, affected 33 million people, and caused damage estimated at around $30 billion. A UNESCO review published in August 2025 found that Pakistan has built a strong technical foundation for flood forecasting, and that the remaining gap lies in the last mile: translating forecasts into localised, actionable warnings that reach communities even when power and mobile networks fail. This is precisely the kind of problem a well designed AI challenge exists to solve, and Pakistan’s National Disaster Management Authority now runs a dedicated technical early warning wing that applies AI and machine learning to multi hazard monitoring. The Pakistan Meteorological Department, with UNESCO support, has begun testing a low cost, AI assisted flood prediction and early warning system in Kalam, Swat, a first of its kind for the country.
What distinguishes Pakistan is that it has backed this problem driven instinct with a complete policy architecture at remarkable speed. On July 30, 2025, the federal cabinet unanimously approved the country’s first National Artificial Intelligence Policy, built on six pillars covering the innovation ecosystem, skills, trustworthy AI, sectoral transformation, infrastructure, and international partnership. The policy sets headline targets that few countries of comparable income have attempted: training one million AI professionals by 2030, producing 1,000 indigenous AI products within five years, launching 50,000 civic AI projects, and offering 3,000 scholarships a year. Financing is ring fenced through a National AI Fund drawn from Ignite’s research and development budget, complemented by AI Innovation and Venture Funds intended to pull in private capital. At Indus AI Week in February 2026, the prime minister went further, committing $1 billion of public investment in AI by 2030, alongside 1,000 fully funded PhD scholarships and a nationwide programme to train one million non IT professionals in AI skills.
Crucially, Pakistan is already running the challenge model, not merely legislating for it. Ignite, the National Technology Fund under the Ministry of IT and Telecom, launched the AI Wrapper Competition 2025 with a prize pool of Rs 8.75 million, among the most significant technology contests in the country’s history. Teams competed across five cities, Karachi, Lahore, Quetta, Peshawar, and Islamabad, in five domains chosen for national importance: education, health, governance, financial inclusion, and climate change. The design reads like a checklist of the principles described earlier in this article. Every entry required a working prototype rather than a slide deck; solutions had to address a local Pakistani challenge; off the shelf products without significant customisation were disqualified; and, most tellingly, all intellectual property remained with the participants. The competition aimed to build a community of more than 500 AI fellows and identify over 25 scalable prototypes, and its November 2025 finale produced national winners with names that suggest genuine ambition: EVA-00 in education, DxVision AI in health, KissanBot, an AI farmer assistant, in agriculture and climate, and PakLawAssist in governance. Indus AI Week in February 2026 extended the model with the Uraan AI Techathon, the country’s first national AI techathon, whose winners move into incubation and mentorship rather than simply collecting trophies.
The infrastructure around the challenge is equally deliberate. Eight National Incubation Centres, including specialised incubators for agritech in Faisalabad and aerospace technologies in Rawalpindi, have incubated more than 1,300 startups over five years; their graduates have created over 126,000 jobs, attracted committed investment of Rs 22 billion, and generated combined revenue of Rs 13.85 billion. The Pakistan Startup Fund offers equity free grants of $50,000 to $1 million, covering up to 30 percent of an investment round once a startup has secured private backing, a design that uses public money to unlock private capital rather than replace it. The National Centre of Artificial Intelligence at NUST, operating laboratories across six universities, has already developed 221 AI products and designs for sectors from precision agriculture to disaster management. The Presidential Initiative for Artificial Intelligence and Computing, launched in December 2018, has trained more than 100,000 Pakistanis in AI and cloud technologies, many of them at no cost. Around these anchors sit further layers of commitment: Special Technology Zones offering ten year exemptions from income tax, minimum tax, and customs duties, plus full retention of foreign currency earnings; a Rs 4.8 billion National Semiconductor Human Resource Development Programme to train engineers in chip design; the Digital Nation Act of January 2025 creating a National Digital Commission; and a 5G spectrum auction in March 2026 that raised roughly $510 million and will carry the connectivity on which every deployed solution depends.
The world has noticed. Pakistan was named Tech Destination of the Year at GITEX Global 2024, entered the top tier of the International Telecommunication Union’s Global Cybersecurity Index, and in April 2025 became the first country to host the Digital Foreign Direct Investment Forum under the World Economic Forum and Digital Cooperation Organisation initiative, drawing participants from 45 countries and securing more than $700 million in investment commitments.
Pakistan’s deepest strengths, however, are demographic and commercial. It is one of the youngest countries on earth: roughly two thirds of its 240 million people are under 30, and the median age is around 21. That youth bulge is already monetising itself. Pakistan is the world’s fourth largest freelancing market, with some 2.37 million registered freelancers whose export earnings crossed $1 billion for the first time in fiscal 2026, a surge of more than 50 percent in a single year. The sector as a whole has become the country’s largest services export category: IT exports reached a record $4.6 billion in fiscal 2025-26, up 21 percent from $3.81 billion the year before, with June 2026 setting an all time monthly record of $416 million. Under the Uraan Pakistan national economic plan, the government has set a target of $10 billion in IT exports by fiscal 2029, and the 2026-27 budget extended the sector’s preferential tax regime for three further years. A country that can field millions of digitally literate young workers, a rapidly compounding export sector, and a government willing to attach prizes, funds, zones, and incubators to public problems is not merely a candidate for the challenge model. It is, in many ways, the model’s ideal testing ground: a place where the needs are urgent, the talent is abundant, the policy machinery is new and unencumbered by legacy, and every successful challenge is immediately a company, a curriculum, and an export.
Four approaches, one logic
| Country | Flagship instrument | Distinctive feature | Wider return sought |
| United Kingdom | £100 million AI competition for public service priorities | No turnover or trading history barriers; suppliers keep their intellectual property | Turns public buying into industrial policy and grows domestic AI firms |
| Singapore | Global FinTech Hackcelerator 2026 (S$80,000 per winner) | Problems sourced from banks and insurers; mentorship, stipends, and investor access built in | Positions the country as a customer and capital hub for founders |
| South Korea | K-Startup Grand Challenge (2,626 teams applied in 2025) | Visas, registration, and settlement support for overseas founders | Converts global talent discovery into local operations and jobs |
| Pakistan | National AI Policy 2025, AI Wrapper Competition, Pakistan Startup Fund, eight National Incubation Centres | Nationwide challenge across five cities and five national domains; participants keep all intellectual property; $1 billion public commitment by 2030 | Turns urgent national needs such as flood warning into companies, skills, exports, and sovereign capability |
The cases point to complementary choices: the UK opens public demand to young firms, Singapore connects founders with customers and capital, Korea supports local establishment, and Pakistan shows what happens when a country treats challenges as the operating system of an entire national AI strategy. A country can combine these elements around its own strengths and needs.
Implications for decision makers: decide what the country should gain
The national objective should shape the challenge from the outset. Five lessons follow from the four cases.
1. Choose problems with a wider market. The UK’s computing and security challenges address needs across sectors. Pakistan’s focus on climate, agriculture, and governance does the same for a country whose solutions could serve much of the developing world. Look for problems whose solutions could serve many customers, support exports, or strengthen an industry the country wants to develop.
2. Offer access that matters. Singapore puts customers and investors within reach. Specify the users, information, facilities, and expertise participants can access. Match funding and payment terms to the firms the challenge aims to attract, as Pakistan’s Startup Fund does by tying public grants to private commitment.
3. Keep capability in the country. DARPA’s released tools show how benefits can spread beyond finalists. Pakistan’s insistence that competitors retain their intellectual property, and that prototypes rather than papers decide winners, applies the same principle. Give local researchers and practitioners substantive roles, and identify test resources or technical lessons others can reuse.
4. Make international participation a route to local activity. Korea couples competition with establishment support. Give promising overseas teams a clear route to customers, partners, and local operations. Measure employment, research, and business activity alongside participation.
5. Track the returns separately. Service improvement, company growth, technical capability, and talent attraction require different evidence. Define the gains the challenge seeks and follow them beyond the award. This keeps the wider ambition accountable without reducing success to a single winner. A challenge deserves public support when it builds something the country can use again.
Conclusion
A successful AI challenge must ultimately leave a country with far more than a single winning prototype. When governments deliberately structure competitions around lasting economic gains, provide direct access to public buyers, open infrastructure, and give international builders a credible route to local operations, a pressing civic problem becomes enduring national capability. The four countries examined here prove the point from four directions. Britain shows that opening public demand to young firms is itself industrial policy. Singapore shows that a challenge wrapped in customers and capital is diplomacy. South Korea shows that selection without settlement is a photo opportunity. And Pakistan shows the deepest truth of all: that necessity, properly organised, is the most powerful innovation policy ever devised.
Pakistan’s experience carries a lesson the wealthier world would do well to study. A nation that has lived through catastrophic floods does not run competitions for vanity, nor does it write AI strategies as conference décor. It builds them the way it must build everything else, quickly, frugally, and aimed squarely at survival first and prosperity second. In doing so it has stumbled, almost by accident, onto the correct sequence: name the real problem, open it to everyone, let the builders keep what they build, and measure success in jobs, exports, and warnings that arrive in time. That sequence is available to any government willing to adopt it.
The true measure of success is not the software delivered on final demonstration day. It is whether, years later, the country can look at its hospitals, its farms, its floodplains, and its balance of payments and find the fingerprints of the challenges it once ran. The nations that master this instrument will not merely use artificial intelligence. They will be shaped by it on their own terms, with their own talent, their own companies, and their own solutions to whatever challenge arrives next. That, in the end, is what national advantage means.
