Author: Chu Hai Qu Incubator
The rules of the game for startups have completely changed.
In Y Combinator (YC)'s latest Spring 2026 "Request for Startups" (RFS), we see a clear signal: AI-native is no longer just a marketing buzzword, but the foundational logic for building the next generation of giants. Startups can now challenge areas once considered "unshakable" with greater speed and lower costs.
This time, YC is not only focusing on software but also casting its gaze on 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 sectors that YC is closely watching and eager to invest in.
1. "Cursor" for Product Managers (Cursor for Product Managers)
In recent years, tools like Cursor and Claude Code have revolutionized the way code is written. But this boom has masked a more fundamental problem: writing code is just the 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, unquantifiable market feedback, and countless Jira tickets. This process is highly manual and full of disconnects.
The market urgently needs an AI-native system that can assist product managers the way Cursor assists programmers. Imagine a tool: you upload all customer interview recordings and product usage data, then ask it, "What should we do next?"
It wouldn't just give you a vague suggestion but would output a complete feature outline, backed by specific customer feedback to justify the decision. Going a step, it could even generate UI prototypes directly, adjust data models, and break down specific development tasks for AI Coding Agents to execute.
As AI gradually takes over the actual code implementation, the ability to "define the product" will become more important than ever. We need a super-tool that closes the loop from "requirement discovery" to "product definition."
2. Next-Generation AI-Native Hedge Funds (AI-Native Hedge Funds)
In the 1980s, when a few funds began experimenting with computer analysis of markets, Wall Street scoffed. Today, quantitative trading is the norm. If you haven't realized we are at a similar inflection point now, you might miss the next Renaissance Technologies or Bridgewater.
This wave isn't about "bolting on" AI to existing fund strategies, but about building AI-native investment strategies from the ground up.
Although existing quant giants have vast resources, their movements are too slow in the博弈 between compliance and innovation. The hedge funds of the future will be driven by swarms of AI agents—they will be able to, like human traders, sift through 10-K filings, listen to earnings calls, analyze SEC documents, and synthesize analyst views to trade, 24/7.
In this field, the true Alpha will belong to the new players who dare to let AI deeply take over investment decisions.
3. The Software Transformation of Service Companies (AI-Native Agencies)
Historically, whether it's design firms, advertising agencies, or law firms, all agency models face a fundamental deadlock: difficulty scaling. Because they sell "people hours," profit margins are low, and growth is dependent on hiring.
AI is breaking this deadlock.
The new generation of agencies will no longer sell software tools to clients, but will instead use AI tools themselves to produce results with 100x efficiency, and then sell the final product directly. This means:
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Design firms can use AI to generate complete customized proposals before signing contracts, delivering a knockout blow to traditional competitors.
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Advertising agencies can use AI to generate cinema-quality video ads without expensive on-location shoots.
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Law firms can draft complex legal documents in minutes, not weeks.
Future service companies will resemble software companies in their business model: possessing the high margins of software companies and near-infinite scalability.
4. Financial Services Derived from Stablecoins (Stablecoin Financial Services)
Stablecoins are rapidly becoming critical global financial infrastructure, but the service layer built on top of 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 window for regulatory arbitrage and innovation.
Currently, users often face a choice between "compliant but low-yield traditional financial products" and "high-yield but high-risk cryptocurrency." The market needs an intermediate form: new financial services built on stablecoins that are both compliant and possess the advantages of DeFi.
Whether it's 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 (Modern Metal Mills)
When people talk about "American reindustrialization," they often focus on labor costs, ignoring the elephant in the room: traditional industrial system design is extremely inefficient.
Take aluminum or steel tube procurement in the US, for example, lead times of 8 to 30 weeks are the norm. This isn't because workers are lazy, but because the entire production management system was designed decades ago. These old factories sacrificed speed and flexibility in pursuit of "tonnage" and "utilization." Additionally, high energy consumption is a major pain point, and factories often lack modern energy management solutions.
The opportunity for reinvention is ripe.
Using AI-driven production planning, real-time Manufacturing Execution Systems (MES), and modern automation technology, we can fundamentally compress lead times and increase profit margins. This isn't 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 the industrial base.
6. AI Upgrade for Government Governance (AI for Government)
The first wave of AI companies has made filling out forms for businesses and individuals astonishingly fast, but this efficiency grinds to a halt when it meets government departments. A flood of digital applications ultimately feeds into government backends that still rely on manual printing and processing.
Government departments urgently need AI tools to handle the impending data deluge. While countries like Estonia have shown a glimpse 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 substantial: once you land your first client, it often means extremely high customer stickiness and huge expansion potential. This is not only a commercial opportunity but also a public good that improves societal operational efficiency.
7. Real-Time AI Mentors for Physical Work (AI Guidance for Physical Work)
Remember the scene in The Matrix where Neo plugs in and instantly learns kung fu? A real-world version of "skill injection" is coming, not through brain-computer interfaces, but through real-time AI guidance.
Instead of debating which white-collar jobs AI will replace, let's see how it can empower blue-collar work. In field service, manufacturing, medical care, and other fields, AI might not be able to "do the work" directly, but it can "see" and "think."
Imagine a worker wearing smart glasses repairing equipment; the AI sees the valve through the camera and says directly in their ear: "Turn off that red valve, use a 3/8-inch wrench, that part is worn and needs replacement."
The maturity of multimodal models, the proliferation of smart hardware (phones, earphones, glasses), and the shortage of skilled labor combine to create this huge demand. Whether it's training systems for existing enterprises or building a new "super blue-collar" labor platform, there is immense room for imagination here.
8. Breaking Language Limits: Large Spatial Models (Large Spatial Models)
Large Language Models (LLMs) drove the AI explosion, but their intelligence is confined to what "language" can describe. To achieve Artificial General Intelligence (AGI), AI must understand the physical world and spatial relationships.
Current AI is still clumsy when handling 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 construct Large Spatial Reasoning Models. These models should not treat geometry as an appendage of language, but as a first principle. Whoever can make AI truly understand and design physical structures has the chance to build the next OpenAI-level foundational model.
9. The Digital Arsenal for Fraud Hunters (Infra for Government Fraud Hunters)
The government is the world's largest buyer, spending trillions of dollars annually, and also loses staggering amounts to fraud. U.S. Medicare alone loses tens of billions of dollars annually 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, who spend years manually organizing documents.
We need intelligent systems designed specifically for this. It's not just a simple dashboard, but an AI detective that can automatically parse messy PDFs, track complex shell company structures, and package scattered evidence into litigable files.
If you can increase the speed of fraud recovery by 10x, you can not only build a vast business empire but also recover tens of billions for taxpayers.
10. Making LLM Training Easy (Make LLMs Easy to Train)
Despite the AI frenzy, the experience of training large models remains appallingly bad.
Developers battle broken SDKs daily, spend hours debugging GPU instances that crash on startup, or find critical bugs in open-source tools. Not to mention the nightmare of handling terabyte-scale datasets.
Just as the cloud computing era gave birth to Datadog and Snowflake, the AI era desperately needs better "picks and shovels." We need:
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APIs that completely abstract the training process.
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Databases that can easily manage hyper-scale datasets.
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Development environments designed specifically for machine learning research.
As "post-training" and model specialization become increasingly important, this infrastructure will become the foundation of future software development.
