Besides writing code, AI is reshaping the world in these 10 overlooked tracks.

The rules of the entrepreneurial game have completely changed.

In Y Combinator's (YC) latest 2026 Spring "Startup Wish List" (RFS), we see a clear signal: AI-native is no longer just a marketing term, but the foundational logic for building the next generation of giants. Today's startups can challenge areas once thought "untouchable" at a faster pace and lower cost.

This time, YC is not only focusing on software but also turning its attention to industrial systems, financial infrastructure, and government governance. If the last wave of AI was about "generating content," the next wave will be about "solving complex problems" and "reshaping the physical world."

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

1. "Cursor" for Product Managers

In the past few 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; figuring out "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 extremely manual and full of gaps.

There is an urgent need for an AI-native system that can assist product managers just as Cursor assists programmers. Imagine a tool where you upload all customer interview recordings and product usage data, then ask it: "What should we do next?"

It won't just give you a vague suggestion; it will output a complete feature outline and justify the decision with specific customer feedback. Furthermore, it could even generate UI prototypes, adjust data models, and break down specific development tasks for an AI Coding Agent to execute.

As AI gradually takes over specific code implementations, the ability to "define products" will become unprecedentedly important. We need a super tool that can bridge the gap from "demand discovery" to "product definition."

2. Next-Generation AI-Native Hedge Funds

In the 1980s, when a few funds began to use computers to analyze the market, Wall Street scoffed. Today, quantitative trading is standard. If you haven't realized that we are at a similar turning point now, you might miss the next Renaissance Technologies or Bridgewater.

This wave of opportunity lies not in "plugging" AI into existing fund strategies but in building AI-native investment strategies from scratch.

While existing 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 that can, like human traders, continuously sift through 10-K reports, listen to earnings calls, analyze SEC filings, and synthesize various analysts' opinions to make trades.

In this field, true Alpha returns will belong to those new players who dare to let AI take deep control of investment decisions.

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

For a long time, whether it’s design firms, advertising agencies, or law firms, all agency models face a deadlock: scalability is difficult. They sell "man-hours," have low profit margins, and growth must rely 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 100 times the efficiency and then sell the final product directly. This means:

Design firms can generate a complete set of customized solutions with AI before signing contracts, significantly undercutting traditional competitors.
Advertising agencies can create cinematic video ads with AI without expensive on-site shoots.
Law firms can draft complex legal documents in minutes instead of weeks.

In the future, service companies will resemble software companies in their business models: enjoying high margins and unlimited scalability.

4. Financial Services Derived from Stablecoins (Stablecoin Financial Services)

Stablecoins are rapidly becoming a key infrastructure in global finance, but the service layer built on them remains a wasteland. With the advancement of bills like GENIUS and CLARITY, 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 have to choose between "compliant but low-yield traditional financial products" and "high-yield but high-risk cryptocurrencies." The market needs a middle ground: new financial services built on stablecoins that are both compliant and have the advantages of DeFi.

Whether providing 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 people talk about "reindustrialization of America," they often focus on labor costs, overlooking the elephant in the room: traditional industrial system designs are extremely inefficient.

Take the procurement of aluminum or steel pipes in the U.S. as an example; delivery cycles of 8 to 30 weeks are the norm. This is not because workers are lazy, but because the entire production management system was designed decades ago. These old factories sacrifice speed and flexibility for "tonnage" and "utilization." Additionally, high energy consumption is a major pain point, and factories often lack modern energy management solutions.

The opportunity for reconstruction is ripe.

By utilizing AI-driven production planning, real-time Manufacturing Execution Systems (MES), and modern automation technologies, we can fundamentally compress delivery cycles and improve profit margins. This is not just about making factories run faster; it’s about making domestic metal production cheaper, more flexible, and more profitable through software-defined manufacturing processes. This is a key part of rebuilding industrial foundations.

6. AI Upgrade for Government Governance (AI for Government)

The first wave of AI companies has made the speed of filling out forms for businesses and individuals astonishing, but this efficiency comes to a halt when it encounters government departments. A large number of digital applications ultimately flow into government backends that still rely on manual printing and processing.

Government departments urgently need AI tools to cope with the impending data deluge. While countries like Estonia have already demonstrated the prototype of a "digital government," this logic needs to be replicated worldwide.

Selling software to the government is indeed a tough nut to crack, but the rewards are equally rich: once you land your first client, it often means extremely high customer stickiness and enormous expansion potential. This is not just a business opportunity but also a public good that enhances the efficiency of social operations.

7. Real-Time AI Guidance for Physical Work (AI Guidance for Physical Work)

Remember the scene in "The Matrix" where Neo learns kung fu in an instant? The real-world version of "skill injection" is coming, and the medium is not a brain-machine interface but real-time AI guidance.

Instead of endlessly discussing which white-collar jobs AI will replace, let’s look at how it can empower blue-collar work. In fields like field service, manufacturing, and healthcare, while AI cannot directly "do" the work, it can "see" and "think."

Imagine a worker wearing smart glasses repairing equipment, and AI sees the valve through the camera and directly tells him in his ear: "Turn off that red valve with a 3/8 inch wrench; that part is worn out and needs to be replaced."

The maturity of multimodal models, the proliferation of smart hardware (phones, headphones, glasses), and the shortage of skilled labor have combined to create this huge demand. Whether providing training systems for existing companies or establishing a brand new "super blue-collar" labor platform, there is vast imaginative space here.

8. Large Spatial Models Breaking Language Limitations (Large Spatial Models)

Large Language Models (LLMs) have driven the explosion of AI, 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 spatial tasks like geometry, 3D structures, and physical rotations. This limits their ability to interact with the physical world.

We are looking for teams that can build Large Spatial Models. These models should not treat geometry as a mere appendage to language but as a first principle. Whoever can make AI truly understand and design physical structures will have the opportunity to establish the next foundational model at the level of OpenAI.

9. Digital Arsenal for Government Fraud Hunters (Infra for Government Fraud Hunters)

The government is the world's largest buyer, spending trillions of dollars each year, while also suffering significant losses due to fraud. In the U.S. alone, Medicare loses billions of dollars each year due to improper payments.

The U.S. False Claims Act allows private citizens to sue fraudulent companies on behalf of the government and receive a share of the recovered funds. This is one of the most effective means of combating fraud, but the current process is extremely primitive: whistleblowers provide leads to law firms, which spend years manually sorting through documents.

We need an intelligent system specifically designed for this purpose. It should not be a simple dashboard but an AI detective capable of automatically parsing chaotic PDFs, tracking complex shell company structures, and packaging scattered evidence into litigable documents.

If you can increase the speed of fraud recovery by tenfold, you can not only build a massive business empire but also recover billions in losses for taxpayers.

10. Making LLM Training Easy (Make LLMs Easy to Train)

Despite the AI boom, the experience of training large models remains horrendously poor.

Developers struggle daily with broken SDKs, spending hours debugging GPU instances that crash as soon as they start, or discovering fatal bugs in open-source tools. Not to mention the nightmare of handling TB-scale data.

Just as the cloud computing era birthed Datadog and Snowflake, the AI era urgently needs better "shovels." We need:

APIs that completely abstract the training process.
Databases that can easily manage massive datasets.
Development environments designed specifically for machine learning research.

As "post-training" and model specialization become increasingly important, this infrastructure will become the cornerstone of future software development.

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