Author: Going Global to Incubators
The rules of the game for entrepreneurship have been completely changed.
In Y Combinator's (YC) latest Spring 2026 Startup Wishlist (RFS), we see a clear signal: AI-native is no longer just a marketing term, but the fundamental logic for building the next generation of giants. Today's startups can challenge areas once considered "unshakeable" at a faster pace and at a lower cost.
This time, Y Combinator 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," then the next wave will be about "solving complex problems" and "reshaping the physical world."
Here are 10 core sectors that Y Combinator is closely monitoring and eager to invest in.
1. "Cursor" designed for product managers.
In the past few years, tools like Cursor and Claude Code have revolutionized the way we write code. But this boom has masked a more fundamental issue: writing code is just a means to an end; figuring out "what exactly to build" is the core.
Currently, product discovery is still in the "Stone Age." We rely on fragmented user interviews, market feedback that is difficult to quantify, and countless Jira tickets. This process is extremely manual and fragmented.
The market urgently needs an AI-native system that can assist product managers in the same way Cursor assists programmers. Imagine a tool where you upload all your customer interview recordings and product usage data, then ask it, "What's our next step?"
It won't just give you a vague suggestion; instead, it will output a complete feature outline and use specific customer feedback to justify the rationale behind the decisions. Furthermore, it can even directly generate UI prototypes, adjust data models, and break down specific development tasks for the AI Coding Agent to execute.
As AI gradually takes over the specific code implementation, the ability to "define products" will become more important than ever before. We need a super tool that can create a closed loop from "requirements discovery" to "product definition".
2. Next-generation AI-native hedge funds
In the 1980s, when a few funds began experimenting with computer-based market analysis, Wall Street scoffed. Today, quantitative trading is standard practice. If you haven't realized we're at a similar turning point, you might be missing out on the next Renaissance Technologies or Bridgewater Associates.
This opportunity lies not in "plugging" AI into existing fund strategies, but in building AI-native investment strategies from scratch.
While existing quantitative giants possess vast resources, they are too slow to act in the struggle between compliance and innovation. Future hedge funds will be driven by swarms of AI agents—capable of 24/7, like human traders, analyzing 10-K reports, listening to earnings calls, examining SEC filings, and synthesizing analyst opinions to make trades.
In this field, the real alpha returns will belong to the new players who dare to let AI deeply take over investment decisions.
3. Software transformation of service-oriented companies (AI-Native Agencies)
For a long time, all agency models, whether design firms, advertising agencies, or law firms, have faced a dead end: difficulty in scaling. This is because they sell "personal time," resulting in low profit margins, and growth depends on recruitment.
AI is breaking this deadlock.
The next generation of agents will no longer sell software tools to clients; instead, they will leverage AI tools to produce results 100 times more efficiently and then directly sell the final product. This means:
Design firms can use AI to generate a complete customized solution before signing a contract, giving them a significant advantage over traditional competitors.
Advertising agencies can use AI to generate cinematic video ads without the need for expensive location shooting.
Law firms can draft complex legal documents in minutes rather than weeks.
Future service companies will resemble software companies more in terms of business model: they will have the high profit margins of software companies, as well as unlimited scalability.
4. Stablecoin Financial Services
Stablecoins are rapidly becoming a key infrastructure of global finance, but the service layer above them remains largely undeveloped. With the advancement of legislation such as GENIUS and CLARITY, stablecoins are at the intersection of DeFi (decentralized finance) and TradeFi (traditional finance).
This presents a huge window for regulatory arbitrage and innovation.
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: a new type of financial service built on stablecoins that is both compliant and possesses the advantages of DeFi.
Whether it's offering higher-yielding savings accounts, tokenized real-world assets (RWA), or more efficient cross-border payment infrastructure, now is the perfect time to connect these two parallel worlds.
5. Reshaping the Old Industrial System: Modern Metal Mills
When people talk about “American reindustrialization,” they often focus on labor costs but overlook the elephant in the room: the traditional industrial system design is extremely inefficient.
In the US, for example, delivery cycles of 8 to 30 weeks are common for aluminum or steel pipe procurement. This isn't due to worker laziness, but rather because the entire production management system was designed decades ago. These outdated factories sacrifice speed and flexibility in pursuit of "tonnage" and "utilization rate." Furthermore, high energy consumption is a major pain point, and factories often lack modern energy management solutions.
The opportunity for restructuring is ripe.
By leveraging AI-driven production planning, real-time manufacturing execution systems (MES), and modern automation technologies, we can fundamentally reduce lead times and improve profit margins. This is not just about making factories run faster, but about making domestic metal production cheaper, more flexible, and more profitable through software-defined manufacturing processes. This is a crucial step in rebuilding the industrial base.
6. AI Upgrades in Government Governance (AI for Government)
The first wave of AI companies has enabled businesses and individuals to fill out forms at an astonishing speed, but this efficiency comes to a halt when it comes to government departments. A large number of digital applications ultimately end up in government back-end systems that still rely on manual printing and processing.
Governments urgently need AI tools to cope with the impending data deluge. While countries like Estonia have already demonstrated the beginnings of a "digital government," this model needs to be replicated worldwide.
Selling software to governments is indeed a tough nut to crack, but the rewards are equally substantial: once you secure your first client, it often means extremely high customer loyalty and enormous expansion potential. This is not only a business opportunity, but also a public service initiative to improve the efficiency of society.
7. Real-time AI Guidance for Physical Work
Remember the scene in "The Matrix" where Neo instantly learns kung fu after being plugged in? A real-life version of "skill injection" is on the horizon, but the medium isn't a brain-computer interface; it's real-time AI guidance.
Instead of spending all day discussing which white-collar jobs AI will replace, let's look at how it empowers blue-collar jobs. In fields such as field service, manufacturing, and healthcare, AI cannot directly "take action," but it can "see" and "think."
Imagine a worker wearing smart glasses repairing equipment. AI sees a valve through a camera and says directly in his ear, "Turn off that red valve. Use a 3/8-inch wrench. That part is worn and needs to be replaced."
The maturity of multimodal models, the widespread adoption of smart hardware (smartphones, headphones, glasses), and the shortage of skilled labor have combined to create this enormous demand. Whether it's providing training systems for existing companies or building a completely new "super blue-collar" workforce platform, there is immense potential here.
8. Large Spatial Models that Break Through Language Limitations
Large Language Models (LLMs) have fueled the AI 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 remains clumsy when dealing with spatial tasks such as geometry, 3D structures, and physical rotations. This limits its ability to interact with the physical world.
We are looking for teams capable of building large spatial models. These models should not treat geometry as an adjunct to language, but rather as a first principle. Whoever enables AI to truly understand and design physical structures will have the opportunity to build the next OpenAI-level foundational models.
9. The Digital Arsenal of Fraud Hunters (Infra for Government Fraud Hunters)
Governments are the world's largest buyers, spending trillions of dollars annually, but also suffer huge losses due to fraud. The U.S. Medicare system alone loses tens of 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 rudimentary: whistleblowers provide leads to law firms, which then spend years manually compiling the necessary documents.
We need an intelligent system specifically designed for this purpose. It's not a simple dashboard, but an AI detective that can automatically parse messy PDFs, track complex shell company structures, and package fragmented evidence into litigable documents.
If you could increase the speed of fraud recovery by 10 times, you could not only build a vast business empire, but also save taxpayers billions of dollars.
10. Make LLMs Easy to Train
Despite the booming development of AI, the experience of training large models remains appallingly bad.
Developers struggle daily with broken SDKs, spend hours debugging GPU instances that crash right after startup, or discover fatal bugs in open-source tools. Not to mention the nightmares of dealing with terabytes of data.
Just as the cloud computing era gave birth to Datadog and Snowflake, the AI era urgently needs a better "shovel." We need:
An API that completely abstracts the training process.
A database that can easily manage extremely large datasets.
A development environment specifically designed for machine learning research.
As post-training and model specialization become increasingly important, these infrastructures will become the cornerstone of future software development.
