Question

From Law and Product Operations to AI-Native Companies: Sean Shen’s Developer Journey

“The important question is: Is this actually worth building?”

What happens when someone with a background in law and product operations suddenly discovers that AI has made software development accessible enough for him to start building products himself?

For Sean Shen, founder of AIOMNIU, the answer has been a rapid transition from product operations and entrepreneurship into AI-assisted development, startup building, marketplaces and AI services.

Sean is based in China and studied law, but never worked professionally in the legal field. Most of his career was spent in product operations and management systems, giving him exposure to product development and system architecture.

He also worked across a remarkably wide range of industries, including apparel, real estate, education, healthcare, pets, food, hospitality, travel, state-owned enterprises and entertainment services.

But none of those experiences gave him the breakthrough he was looking for.

Then AI arrived.

And suddenly, someone with his background could begin building software.

That changed the question.

Instead of asking whether he had enough technical people to build an idea, Sean could start asking something much more fundamental:

Is the idea actually worth building?

From Law to Product Operations

Sean's journey into technology wasn't a conventional developer story.

He studied law but ultimately moved into product operations, where he spent years working with management and back-office systems.

That experience gave him something valuable even though he wasn't writing most of the code himself: an understanding of products, business processes and system architecture.

His career also exposed him to many different industries.

He worked around apparel, real estate sales, education, healthcare, pets, food, wigs, adult products, hospitality, travel, state-owned enterprises and entertainment services.

The diversity of those experiences eventually became useful.

He had seen many different types of businesses and operational problems.

But for a long time, building software himself wasn't really an option.

Then AI changed the equation.

The Moment AI Made Development Feel Accessible

One of the earliest moments that influenced Sean happened in 2023, when he was working at an online medical consultation app in China.

His responsibility covered the entire consultation business.

At the time, the company was already experimenting with AI to improve the pre-consultation process through a project called Medical Assistant Integration.

GPT had just appeared.

The developers around him were excited about it, although Sean admits he didn't really understand GPT at first.

What caught his attention was seeing an AI-powered autocomplete feature actually working inside the product.

He remembers telling a colleague:

“This is probably the thing that can actually make the consultation process more efficient in the future. You just need to make this work really well.”

That experience stayed with him.

After leaving the company, Sean continued building businesses, mainly in e-commerce.

But entrepreneurship gradually convinced him that he wanted to move toward services rather than physical products.

Physical products require inventory, capital and heavier assets.

Services can be adapted and iterated much faster.

And then AI created another possibility: perhaps software development itself could become a service that someone like him could participate in.

The Real Reason He Started Building With AI

Sean doesn't try to turn his origin story into something more inspirational than it was.

His motivation was surprisingly straightforward.

He wanted to make money.

When he realized that AI could enable someone working in product operations to take on website development projects, he started thinking about the larger opportunity.

If AI could enable more people to build software, there could eventually be a huge wave of AI-powered developers.

And if that productivity could be connected to businesses, perhaps it could become a service business.

Sean has also long believed that B2B customers are more willing to pay for AI than consumers.

That became one of the ideas behind his direction with AIOMNIU.

The Dark Forest and the Little Mouse

Sean describes the market as a dark forest.

In that environment, he doesn't believe the biggest player necessarily has the only path to survival.

Instead, he uses a different metaphor:

“The best entrepreneur may not be the T. rex.”

“Maybe it’s the little mouse that keeps adapting and evolving.”

That idea captures an important part of Sean's approach.

Rather than trying to compete directly with the largest AI companies, he is interested in finding smaller, specialized opportunities where a small team can adapt quickly.

This thinking becomes particularly important when looking at the AI application layer.

Building in Public Is a Growth Strategy

Sean has also been publicly documenting his entrepreneurial journey, including his Only10 series.

For him, building in public isn't primarily about telling a romantic startup story.

It's about distribution.

AIOMNIU is an early-stage project without a major established brand or a natural source of traffic.

That means the team needs to create ways for people to discover what they are building.

Sean sees content as an unusually efficient asset for a small team.

One piece of content can:

• Communicate an idea
• Be distributed across multiple social platforms
• Accumulate search value
• Contribute to backlink growth
• Attract potential users
• Create another channel for learning about the market

For a small team with limited resources, one piece of work can therefore create multiple kinds of value.

That's why Sean describes building in public very simply:

“It’s fundamentally a growth strategy.”

Focus Doesn't Mean Never Changing Direction

Sean has written about turning down several seemingly good opportunities while trying to stay focused.

But he makes an important distinction.

Focus, in his view, doesn't mean locking yourself into one direction and refusing to change.

If a genuinely good opportunity appears, it shouldn't automatically be ignored.

The real question is timing and long-term value.

Sean defines focus this way:

“In a constantly changing environment, make the choice that has the highest long-term compounding value, and commit your resources to it.”

A focused person can still encounter tempting opportunities.

The difference is that after evaluating them, they decide whether the opportunity deserves resources that would otherwise go toward the core business.

So focus isn't about refusing to change.

It's about preventing constant change from diluting the resources needed to build something meaningful.

What Building AI Products Has Taught Him

Sean has already experimented with several AI products, including an AI resume builder and an AI resume screening tool.

Those experiments have led him to a strong view about the limitations of API-based AI products.

He believes AI products may have shorter lifespans than many traditional software products.

The reason is simple.

If the core value of a product is essentially wrapping the capabilities of a foundation model in a user interface, improvements in those underlying models can eventually reduce the product's differentiation.

Sean compares this situation to:

“Drinking poisoned wine in the desert.”

There may be good reasons to use an API when validating an idea or keeping early costs low.

But he believes that once an AI product has a meaningful user base and enough accumulated data, the long-term direction should increasingly move toward specialized models, local deployment, proprietary infrastructure or post-training.

The goal isn't to become another GPT.

It is to become a specialist.

“We just need to become the specialist in a niche.”

The Opportunity After Foundation Models

Sean sees the foundation-model layer as extremely difficult for independent developers to compete in.

Companies such as GPT, Claude, GLM and DeepSeek are already operating at enormous scale, while their capabilities and pricing continue to evolve.

For a small team, building an entire business around one particular foundation model can therefore introduce instability.

Instead, Sean is particularly interested in what happens as the foundation-model ecosystem becomes more mature.

He expects opportunities to emerge around smaller, specialized models trained around specific industries, businesses and use cases.

The advantage won't necessarily come from building the biggest model.

It may come from understanding a specific domain better than anyone else.

“We just need to become the specialist who truly understands a particular domain, its data, and its real-world use cases.”

Sean is particularly watching areas such as:

• AI applications across different industries
• AI-powered business growth
• Data compliance
• AI security
• Specialized AI services
• Industry-specific applications

The Application Layer Hasn't Fully Exploded Yet

Sean believes the massive expansion of AI applications may still be ahead.

The infrastructure layer has received enormous attention, but he sees another opportunity emerging as the cost and accessibility of AI infrastructure continue to change.

For independent developers and small teams, this could create room to build specialized applications without needing to compete with the companies developing the underlying foundation models.

The opportunity is not necessarily to build another general-purpose AI.

It is to identify a specific problem where AI can create meaningful economic value.

From AI to Businesses — and Eventually AI to AI

Sean has an interesting perspective on how AI and humans may interact as AI becomes increasingly embedded in production systems.

He doesn't believe humans will simply be replaced as a whole.

Instead, he expects many individual jobs and roles to be redefined.

He describes a possible progression in three stages.

AI to B

The first stage is AI to B — AI entering businesses and society's production systems and beginning to generate real economic and social value.

AI to AI

The second stage is AI to AI.

Once enough businesses become AI-enabled, AI systems may increasingly call on other AI systems to provide services and further increase productivity.

Human to AI

The third stage is what Sean calls Human to AI.

As productivity gains from digital AI systems eventually encounter diminishing returns, humans may continue to provide forms of value that AI cannot fully replace.

In that scenario, AI wouldn't simply assist humans.

Sean imagines AI increasingly calling on humans.

Some early examples already exist where AI systems depend on humans for research, real-world information and physical-world experiences.

But Sean imagines something broader in the long term:

A continuing working relationship where humans and AI each contribute what they are uniquely capable of contributing.

The Biggest Challenge: Growth

For Sean, the biggest challenge so far has been growth.

AIOMNIU aims to become a global platform connecting AI talent, businesses and services.

But reaching global markets is difficult for an early-stage company without an established distribution channel.

Over time, Sean has also come to believe that growth isn't always simply a marketing problem.

Sometimes it comes down to whether the product itself is compelling enough.

That realization has influenced how AIOMNIU experiments.

The team has built products such as AI resume screening partly to attract users and learn what people actually need.

But the strategy is evolving.

Rather than constantly creating another tool simply to attract traffic, Sean wants to get closer to real businesses.

That means understanding:

• How the business makes money
• How its operations work
• Which processes matter
• Where its data lives
• How its systems connect
• How its people collaborate

Only after understanding those things can the team identify where AI can create genuine value.

The Moment AI Changed His Ambition

Sean describes his evolution with AI in three stages.

Before AI, he wouldn't have imagined starting a product with only a handful of people.

A serious software project might require frontend developers, backend developers, product people, operations and other specialists.

Even building a website could eventually mean assembling a large team.

When GPT first appeared, that assumption didn't immediately disappear.

AI initially felt more like having a knowledgeable expert sitting beside him.

Ask a question and get help.

But the emergence of agentic coding tools changed the experience.

Tools such as Claude made it possible for one person to build an MVP much more directly.

For Sean, the bigger impact wasn't simply that AI could write more code.

It was that AI expanded what a small team could realistically attempt.

Previously, when he had an idea, his first question might have been:

“Do I have the ability to organize enough people to build this?”

Now the question is different:

“Is this actually worth building?”

That is a profound shift in how he thinks about entrepreneurship.

What AIOMNIU Is Building

Today, AIOMNIU is focused on two core areas:

AI talent outsourcing and AI services.

The talent side can be thought of as an AI-focused version of a marketplace such as Upwork, connecting businesses with people who can provide AI-related talent and services.

The services side is increasingly moving toward enterprise AI transformation.

But Sean doesn't see enterprise transformation as simply uploading company documents and generating an AI report.

Real transformation requires understanding how the business actually works.

That means looking at:

• Business models
• Operations
• Processes
• Data
• Existing systems
• Internal workflows
• People and collaboration

Only then can AI opportunities be identified in a meaningful way.

AIOMNIU is therefore using content and conversations with businesses to understand what companies actually need from AI.

From there, the team can decide which problems should eventually become products and which are better served through talent and FDE-style services.

Sean describes the resulting cycle as a technology flywheel.

As foundation models improve, the products and services built on top of them can improve as well.

But the part he is most excited about is what happens when the application layer begins to expand at scale.

“I believe that moment will come, and I’m excited to be here for it.”

His Experience With CoderLegion

Sean has also become active in CoderLegion's AI, Open Source and WebDev communities.

He says he has a very positive impression of the platform, particularly its efforts to encourage creators and maintain an active community.

For him, there is an important relationship between creators and the platforms they participate in.

When creators feel that a community provides real value, they contribute more.

That creates more user-generated content, which in turn can strengthen the community.

Sean connects that idea to the spirit of open source:

“A product shouldn’t be treated purely as private property. It can become a shared asset built by everyone who participates in it.”

He hopes CoderLegion continues to maintain that openness as it grows.

Start Now. Just Build.

For someone sitting on an AI product idea and wondering whether they have enough technical knowledge, resources or people to begin, Sean's advice is remarkably direct.

Don't wait for everything to be ready.

Don't wait until you have assembled a large team.

Don't wait until you know exactly how everything will work.

His advice is:

“Start now. Start immediately. Just build.”

That advice makes sense in the context of his own journey.

A person who studied law, spent years in product operations and had almost no development experience until April 2026 is now building AI products, experimenting with agentic development and working toward an AI talent and services platform.

The technology changed what was possible for him.

But perhaps the bigger change was what it allowed him to ask.

The question used to be:

“Can I build this?”

Now it is:

“Is this worth building?”

And that may be one of the most important questions for anyone building software in the AI era.

About Sean Shen

Sean Shen is the founder of AIOMNIU, an AI talent and services platform.

His interests include AI startups, marketplaces, remote work, growth systems and the future of AI-native companies.

His current work focuses on connecting AI talent and businesses, exploring enterprise AI transformation, experimenting with AI-powered products and understanding where specialized AI applications can create real business value.

Sean also shares his entrepreneurial journey publicly through projects and content such as Only10, using content as both a learning mechanism and a growth strategy.

CoderLegion Developer Stories

This story is part of CoderLegion Developer Stories, where developers, founders, engineers and technology professionals share their experiences, challenges, projects and lessons from their journeys.

The goal is to give the CoderLegion community a closer look at the people and ideas behind the technology.

Interviewed and edited by Mehadi Hasan, Community & Editorial Team at CoderLegion.