Besides coding, AI is reshaping the world in these 10 overlooked sectors

Author: Going Overseas Incubator

The rules of the startup game have been completely rewritten.

In Y Combinator (YC)’s latest 2026 Spring “Startup Wishlist” (RFS), we see a clear signal: AI-native is no longer just a marketing buzzword but the fundamental logic for building the next generation of giants. Today’s startups can challenge once “unstoppable” fields at a faster pace and lower cost.

This time, YC is not only focusing on software but also turning its gaze toward industrial systems, foundational financial infrastructure, and government governance. If the previous AI wave was about “content generation,” the next wave will be about “solving complex problems” and “reshaping the physical world.”

Below are the 10 core tracks YC is closely watching and eager to invest in.

1. “Cursor” for Product Managers

In recent years, tools like Cursor and Claude Code have completely changed the way code is written. But this prosperity masks a more fundamental issue: writing code is just a means; understanding “what to build” is the core.

Currently, the product discovery process is still in the “Stone Age.” We rely on fragmented user interviews, hard-to-quantify market feedback, and countless Jira tickets. This process is highly manual and full of gaps.

The market urgently needs an AI-native system that can assist product managers like Cursor assists programmers. Imagine a tool where you upload all customer interview recordings and product usage data, then ask: “What should we do next?”

It won’t just give you a vague suggestion but will output a complete feature outline, supported by specific customer feedback to justify decisions. Furthermore, it can directly generate UI prototypes, adjust data models, and break down specific development tasks for AI Coding Agents to execute.

As AI gradually takes over concrete coding, the ability to “define products” will become more important than ever. We need a super tool that can close the loop from “requirements discovery” to “product definition.”

2. Next-Generation AI-Native Hedge Funds

In the 1980s, a few funds began experimenting with using computers to analyze markets, but Wall Street scoffed. Today, quantitative trading is standard. If you still don’t realize we are at a similar turning point, you might miss the next Renaissance Technologies or Bridgewater.

This wave of opportunity isn’t about adding AI as an “add-on” to existing strategies but building AI-native investment strategies from scratch.

While current quantitative giants have vast resources, their actions are too slow in the game of compliance and innovation. Future hedge funds will be driven by swarms of AI agents—able to analyze 10-K reports, monitor earnings calls, analyze SEC filings, and synthesize analyst opinions around the clock, just like human traders.

In this field, true Alpha returns will belong to those daring enough to let AI deeply take over investment decisions.

3. Software-Driven Transformation of Service Companies (AI-Native Agencies)

All along, whether design firms, advertising agencies, or law firms, agency models face a deadlock: difficulty scaling. They sell “man-hours,” with low profit margins, and growth depends on hiring.

AI is breaking this deadlock.

The new generation of agencies will no longer sell software tools to clients but will use AI tools themselves to produce results at 100x efficiency, then sell the final product directly. This means:

• Design firms can generate customized plans with AI before signing contracts, crushing traditional competitors.
• Advertising firms can produce cinematic-quality videos without expensive on-site shoots.
• Law firms can draft complex legal documents in minutes instead of weeks.

Future service companies will operate more like software companies: high margins and unlimited scalability.

4. Financial Services Based on Stablecoins

Stablecoins are rapidly becoming a key infrastructure of global finance, but the service layer above them remains a wilderness. With bills like GENIUS and CLARITY advancing, stablecoins are at the intersection of DeFi (Decentralized Finance) and TradFi (Traditional Finance).

This is a huge regulatory arbitrage and innovation window.

Currently, users often choose between “low-yield, compliant traditional financial products” and “high-yield, high-risk cryptocurrencies.” The market needs an intermediate form: new financial services built on stablecoins that are both compliant and leverage DeFi advantages.

Whether offering higher-yield savings accounts, tokenized real-world assets (RWA), or more efficient cross-border payment infrastructure, now is the best time to connect these two parallel worlds.

5. Reshaping Old Industrial Systems: Modern Metal Mills

When talking about “American re-industrialization,” people often focus on labor costs but overlook a big elephant in the room: traditional industrial system design is extremely inefficient.

For example, in procurement of aluminum or steel pipes in the US, delivery cycles of 8 to 30 weeks are normal. This isn’t due to lazy workers but because the entire production management system was designed decades ago. These old factories, in pursuit of “tonnage” and “utilization,” sacrifice speed and flexibility. High energy consumption is another pain point, and many lack modern energy management solutions.

The opportunity for reconstruction is ripe.

Using AI-driven production planning, real-time Manufacturing Execution Systems (MES), and modern automation technologies, we can fundamentally shorten delivery cycles and increase profit margins. It’s not just about making factories run faster but redefining manufacturing processes through software, making domestic metal production cheaper, more flexible, and more profitable. This is a key step in rebuilding industrial infrastructure.

6. AI Upgrades for Government Governance

The first wave of AI companies has already made filling out forms for enterprises and individuals astonishingly fast, but this efficiency hits a wall when it comes to government departments. Massive digital applications still funnel into government backends that rely on manual printing and processing.

Governments urgently need AI tools to handle the coming data flood. While countries like Estonia have demonstrated “digital government” prototypes, this logic needs to be replicated worldwide.

Selling software to governments is a tough nut, but the rewards are substantial: once you land the first client, it often means high customer stickiness and huge expansion potential. It’s not just a business opportunity but also a public good to improve societal operations.

7. Real-Time AI Mentors for Physical Work

Remember Neo learning kung fu instantly after plugging into the Matrix in “The Matrix”? The real-world version of “skill injection” is coming, not via brain-machine interfaces but through real-time AI guidance.

Instead of debating which white-collar jobs AI will replace, look at how it empowers blue-collar work. Field service, manufacturing, healthcare—AI can’t directly “do” the work yet, but it can “see” and “think.”

Imagine workers wearing smart glasses, repairing equipment while AI, via camera, sees a valve and says: “Turn off that red valve, use a 3/8-inch wrench, that part is worn out and needs replacing.”

The maturity of multimodal models, the proliferation of smart hardware (phones, headsets, glasses), and the shortage of skilled labor create a huge demand. Whether building training systems for existing companies or creating a new “super blue-collar” workforce platform, there’s enormous potential here.

8. Large Spatial Models to Break Language Barriers

Large Language Models (LLMs) have driven AI’s explosion, but their intelligence is limited to what “language” can describe. To achieve Artificial General Intelligence (AGI), AI must understand the physical world and spatial relationships.

Current AI still struggles with geometry, 3D structures, physical rotations, and other spatial tasks. This limits its ability to interact with the physical world.

We need teams capable of building Large Spatial Models. These models shouldn’t treat geometry as an appendage of language but as a first principle. Whoever enables AI to truly understand and design physical structures will have the chance to create the next foundational model at OpenAI’s level.

9. Digital Arsenal for Government Fraud Hunters

Governments are the world’s largest buyers, spending trillions annually, yet suffer heavy losses from fraud. In the US alone, Medicare loses hundreds of billions annually due to improper payments.

The False Claims Act allows private citizens to sue fraudulent companies on behalf of the government and share in recovered funds. It’s one of the most effective anti-fraud tools, but the current process is primitive: whistleblowers provide clues to law firms, which spend years manually sorting through documents.

We need intelligent systems designed specifically for this purpose. Not just dashboards, but AI detectives capable of parsing chaotic PDFs, tracking complex shell company structures, and packaging scattered evidence into litigable files.

If you can boost fraud recovery speed by 10x, you could build a massive business empire and recover billions for taxpayers.

10. Simplifying LLM Training

Despite the AI boom, training large models remains an incredibly painful experience.

Developers fight broken SDKs daily, spend hours debugging GPU instances that crash on startup, or discover fatal bugs in open-source tools. Handling TB-scale data is a nightmare.

Just as Datadog and Snowflake emerged during the cloud era, the AI era urgently needs better “shovels.” We need:

• Fully abstracted APIs for training processes.
• Databases capable of managing ultra-large datasets easily.
• Development environments tailored for machine learning research.

As “post-training” and model specialization become more critical, these infrastructures will form the foundation of future software development.