The rapid development of artificial intelligence is changing not only how software is built but also how entrepreneurs identify new business opportunities. One example is Sualeh Asif, a Pakistani cofounder of AI coding platform Cursor, whose journey highlights an emerging concept known as “product overhang.”

The idea refers to the gap between what an AI model is already capable of doing and what existing software products actually allow users to accomplish.

The concept has gained attention as AI models become increasingly capable of handling complex coding, research, analysis and automation tasks. For startups, the opportunity may no longer simply be developing better models, but finding practical products that unlock capabilities already available.

Who Is Sualeh Asif?

Sualeh Asif, who is originally from Karachi, is one of the four cofounders of Cursor, an AI-powered coding platform.

Asif completed his A-Levels at Nixor College before receiving a scholarship to the Massachusetts Institute of Technology (MIT). He also represented Pakistan at the International Mathematical Olympiad.

In 2022, Asif and his classmates founded Cursor, focusing on a different approach to software development as generative AI capabilities rapidly improved.

Cursor subsequently became one of the most prominent AI coding platforms, placing its founders at the center of the rapidly expanding AI software industry.

Cursor’s Story Shows How AI “Product Overhang” Works

The idea of product overhang provides an important explanation for why some AI startups can grow quickly even without developing their own foundation model.

According to Anthropic’s Claude Code creator Boris Cherny, modern AI models can possess capabilities that software products do not fully expose to users.

In earlier generations of AI coding tools, developers often used models primarily for autocomplete, code suggestions or answering questions about existing code.

However, increasingly capable models could perform much larger tasks, including creating entire files and working through broader software-development problems.

The opportunity emerged when developers began designing products that gave AI more direct access to the tools and environments required to complete those tasks.

Why Building Software Is Becoming Easier

AI has significantly reduced the amount of time and technical effort required for certain software-development tasks.

A smaller team can now accomplish work that previously required considerably more engineering resources. But this creates a new challenge: knowing what should actually be built.

As the cost of producing software falls, identifying valuable problems could become more important than simply having the ability to write code.

This creates an opportunity for entrepreneurs who understand both technology and customer needs.

For Pakistani startups in particular, the shift could provide an opportunity to compete globally without requiring the enormous capital traditionally associated with technology companies.

Pakistan Already Has a Growing Group of Global AI Founders

The broader story also highlights Pakistan’s contribution to the global startup ecosystem.

Research from the National Foundation for American Policy (NFAP) found that immigrants founded or co-founded 455 of 775 U.S. privately held billion-dollar companies, representing 59% of the total. The study identified founders from 76 countries.

Pakistan was associated with 10 billion-dollar U.S. startups in the research, including companies connected to founders such as Sualeh Asif, Qasar Younis, Samar Abbas, Obaid Khan and others.

The data suggests that international education and access to global technology ecosystems can play an important role in transforming technical expertise into high-growth businesses.

The AI Opportunity May Be Bigger Than Better Prompts

For several years, prompt engineering was widely described as one of the most important AI skills.

But the evolution of AI products suggests that simply writing better instructions may not be enough.

The more valuable skill could be verification — understanding whether an AI-generated result is actually correct and useful.

For example, a developer can ask an AI system to create software, but the developer still needs appropriate tests and evaluation methods to determine whether the resulting application works correctly.

The same principle applies to research, financial analysis, business operations and data science.

Domain knowledge therefore remains important even as AI becomes more capable.

Why AI Verification Is Becoming More Important

AI systems can generate impressive outputs, but they can still make factual, technical or logical mistakes.

That makes reliable feedback loops increasingly important.

Instead of simply asking an AI model to complete a task, developers can provide it with ways to test its own work, compare results and identify failures.

This approach can turn AI from a simple content generator into a more capable working system.

The lesson from modern AI coding products is that the quality of the verification process can be just as important as the quality of the initial instruction.

Anthropic’s “Delete” Approach Offers Another AI Lesson

Boris Cherny has also described an unusual approach to maintaining Claude Code.

As newer AI models become more capable, the team has removed large portions of the instructions previously used to guide the system. Cherny said more than 80% of Claude Code’s system prompt was removed for a recent model generation because many instructions had been compensating for limitations that the newer model could handle itself.

The approach is related to ablation, where components are removed and their impact is measured rather than assuming that adding more instructions will automatically improve performance.

For AI developers, this suggests that older instructions can eventually become unnecessary—or even interfere with a stronger model’s capabilities.

What This Means for Pakistani Entrepreneurs

The lesson for Pakistan’s technology sector extends beyond coding.

Entrepreneurs could look for situations where existing AI models are capable of doing significantly more than current software products permit.

That could include healthcare, education, financial services, customer support, legal research, logistics, agriculture and business analytics.

The opportunity lies in identifying a real problem, determining what today’s AI can already solve and then building a reliable product around that capability.

The Future of AI May Belong to Product Builders

The next generation of AI companies may not necessarily be the organizations that train the biggest models.

Some could instead be startups that discover creative ways to unlock capabilities hidden inside existing models.

Sualeh Asif’s journey with Cursor illustrates how technical education, entrepreneurial judgment and a willingness to experiment can combine to create globally significant technology companies.

For Pakistan, the bigger opportunity is to move beyond simply consuming AI tools and begin building products around them.

The technology is increasingly accessible. The difficult part is recognizing what AI can already do—and turning that capability into something people actually need.