APART FROM WRITING CODES, AI IS RESHAPING THE WORLD ON THESE 10 NEGLECTED TRACKS

Author:Out to sea to the incubator

The rules of entrepreneurship have changed completely。

In Y Combinator (YC)'s latest release, 2026 Spring & ldquo; ” (RFS), we see a clear signal that AI is no longer a mere marketing term, but rather the underlying logic of building the next generation of giants. Today's start-ups can challenge areas that were once considered to be “ unshockable ” at a faster and lower cost。

This time, YC focused not only on software but also on industrial systems, financial bottom-up structures and governance. If the previous wave of AI is about “ generating content ” then the next wave will be about “ solving complex issues ” and “ reshaping the physical world &rdquo。

HERE ARE THE 10 CORE TRACKS THAT YC IS WATCHING CLOSELY AND EAGER TO INVEST。

1. "Cursor for Product Manager"

Over the past few years, tools like Cursor and Claude Code have completely changed the way the code is written. But this boom hides a more fundamental question: writing codes is a means to figure out what &rdquao should be; it is the core。

At present, the process of product discovery is still in “ the Stone Age ” We rely on fragmented user interviews, hard-to-qualify market feedback and numerous Jira worksheets. The process is highly manual and is full of faults。

The market urgently needs an AI primary system that supports product managers like Cursor's auxiliary programmer. Imagine a tool that uploads all the records of client interviews and the data used for the product, and then asks it: “ what do we do next? ”

Rather than giving you a vague proposal, it would export a complete functional outline and justify decision-making through specific client feedback. To go further, it can even directly generate UI prototypes, adjust data models, and decipher specific development tasks to AI Coding Agent。

As AI gradually takes over specific codes, “ defines product ” capabilities will become more important than ever. We need a super tool to get through &ldquao; &rdquao; to &ldquao; product definition &rdquao; closed loop。

2. Next generation AI Native hedge funds

In the 1980s, when a few funds began to attempt to analyse the market with computers, Wall Street snuffed it. Quantified transactions are now framed. If you don't realize that we're at a similar turning point, you might miss the next Renaissance Technologies or Bridgewater。

This wave of opportunities is not about putting AI & ldquo; outer & rdquo; to the existing fund strategy, but about building an AI original investment strategy from scratch。

While the existing quantitative giants have enormous resources, they are moving too slowly in the game of compliance and innovation. Future hedge funds will be driven by a group of AI smarts & mdash; — they will be able, like human traders, to combe the 10-K financial statements 24 hours a day, monitor financial teleconferences, analyse SEC documents and trade with the views of analysts。

In this area, real Alpha returns will belong to new players who dare to let AI take over investment decisions in depth。

3. Software transformation of service companies (AI-Native Companies)

All along, all the agents -- design companies, advertising companies, and firms -- have been facing a dead end: it's hard to scale. Because they sell &ldquao; headtime &rdquao; and have low profitability and have to rely on recruitment for growth。

AI IS BREAKING THIS KNOT。

A NEW GENERATION OF AGENTS WILL NO LONGER SELL SOFTWARE TOOLS TO CLIENTS, BUT WILL USE THE AI TOOL TO PRODUCE 100 TIMES MORE EFFICIENT RESULTS AND THEN SELL THE FINAL PRODUCT DIRECTLY. THIS MEANS:

• DESIGNERS CAN USE AI BEFORE SIGNING CONTRACTS TO GENERATE A WHOLE PACKAGE OF CUSTOMIZATIONS THAT CAN BE USED AGAINST TRADITIONAL COMPETITORS。

• ADVERTISING COMPANIES DO NOT NEED EXPENSIVE FIELD FILMING TO GENERATE FILM-BASED VIDEO ADVERTISEMENTS USING AI。

• A law firm can complete the drafting of complex legal instruments in minutes rather than weeks。

Future service companies will be more like software companies in their business models: high levels of Maori ownership of software companies and unlimited scalability。

4. Stablecoin financial services

The Stablecoins are rapidly becoming a critical infrastructure for global finance, but the services above them remain a wasteland. With the advancement of bills such as GENIUS and Clarity, the stabilization currency is at the crossroads of DeFi (decentralized finance) and TradFi (traditional finance)。

This is a huge regulatory arbitrage and innovation window。

At present, users often make single-choice issues between “ &rdquo, a traditional financial product that is compliant but has low returns; and “ high-yield but high-risk encrypted currency ” and others. Markets need an intermediate form: new financial services based on stable currencies that are both compliant and have the advantage of DeFi。

WHETHER IT IS A SAVINGS ACCOUNT THAT PROVIDES HIGHER RETURNS, A REAL WORLD ASSET (RWA) THAT MONETIZES, OR A MORE EFFICIENT CROSS-BORDER PAYMENT INFRASTRUCTURE, THIS IS THE BEST TIME TO CONNECT THE TWO PARALLEL WORLDS。

5. Reinventing old industrial systems: Modern Metal Mills

when people talk about “ us re-industrialization & rdquo; they tend to stare at the cost of labour while ignoring the elephant in a room: traditional industrial system designs are extremely inefficient。

in the united states, for example, the delivery cycle of 8 to 30 weeks is normal. this is not because workers are lazy, but because the entire production management system was designed decades ago. these old plants have sacrificed speed and flexibility in pursuit of “ tonnage ” and “ utilization &rdquo. in addition, high energy consumption is a major problem, and factories often lack modern energy management programmes。

The opportunity to rebuild has matured。

USING AI-DRIVEN PRODUCTION PLANS, REAL-TIME MANUFACTURING IMPLEMENTATION SYSTEMS (MES) AND MODERN AUTOMATED TECHNOLOGIES, WE CAN FUNDAMENTALLY REDUCE DELIVERY CYCLES AND INCREASE PROFITABILITY. THIS IS NOT JUST TO MAKE FACTORIES RUN FASTER, BUT TO MAKE INDIGENOUS METAL PRODUCTION CHEAPER, MORE FLEXIBLE AND MORE PROFITABLE THROUGH SOFTWARE-DEFINED MANUFACTURING PROCESSES. THIS IS A KEY ELEMENT IN REBUILDING THE INDUSTRIAL BASE。

6. AI for Government

THE FIRST WAVE OF AI HAS MADE IT POSSIBLE FOR BUSINESSES AND INDIVIDUALS TO FILL OUT FORMS AT AN ALARMING RATE, BUT THIS EFFICIENCY STOPS WHEN THEY ENCOUNTER GOVERNMENT DEPARTMENTS. A LARGE NUMBER OF DIGITAL APPLICATIONS WERE EVENTUALLY REMITTED TO A GOVERNMENT BACK OFFICE THAT HAD TO BE MANUALLY PRINTED AND MANUALLY PROCESSED。

The government sector urgently needs an AI tool to cope with the impending data flood. Although countries like Estonia have demonstrated the prototype of “ digital government ” this logic needs to be replicated throughout the world。

The sale of software to the government is indeed a hard-to-eat bone, but the return is equally strong: once you take your first customer, it often means a high degree of customer viscosity and a huge potential for expansion. This is not only a business opportunity, but also a public good to improve the efficiency of the functioning of society。

7. Real-time AI mentors for physics

Remember when Neo put a tube in the hacker empire to learn kung fu? Real-life “ skills injection ” is coming, the carrier is not brain interface, but real-time AI guidance。

Instead of talking all day about what AI would replace with white-collar jobs, let's see how it empowers blue-collar jobs. In the areas of on-site services, manufacturing, medical care, AI, although not directly “ hands-on ” but it can “ see ” and “ think &rdquo。

Imagine that the worker with the smart glasses is repairing the equipment, and AI sees the valve through the camera, and says in his ear: “ shut down the red valve, with a 3/8 inch wrench, the part is worn and needs replacement. ”

the maturity of multi-modular models, the proliferation of smart hardware (cell phones, headphones, glasses) and the shortage of skilled labour have triggered this enormous demand. whether it is a training system for existing enterprises or the creation of an entirely new & ldquo; a super blue collar & rdquo; and a labour platform, there is enormous imagination。

8. Large space models that break language limitations

The Big Language Model (LLM) promotes the outbreak of AI, but their wisdom is limited to “ language ” and can be described. To achieve universal artificial intelligence (AGI), AI must understand the physical world and space relations。

THE CURRENT AI REMAINS CLUMSY IN DEALING WITH SPACE MISSIONS SUCH AS GEOMETRY, 3D STRUCTURES, PHYSICAL ROTATIONS, ETC. THIS LIMITS THEIR ABILITY TO INTERACT WITH THE PHYSICAL WORLD。

We're looking for a team that can build a large space reasoning model. Such models should not treat geometry as an accessory to language, but rather as a primary principle. Whoever can make AI truly understand and design the physical structure has the opportunity to build the next foundation model at OpenAI level。

9. Infra for Government Frad Hunters

Governments are the largest buyers in the world, spending trillions of dollars a year, while losing a lot from fraud. Health insurance in the United States alone loses tens of billions of dollars a year because of improper payments。

The False Claims Act of the United States allows private citizens to sue fraudulent companies on behalf of the Government and obtain 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 the firm, which spends several years co-processing documents。

WE NEED SMART SYSTEMS SPECIFICALLY DESIGNED FOR THAT. IT IS NOT A SIMPLE DASHBOARD. IT IS AN AI DETECTIVE WHO CAN AUTOMATICALLY DECIPHER A CONFUSED PDF, TRACE COMPLEX SHELL STRUCTURES, AND PACKAGE SCATTERED EVIDENCE INTO ACTIONABLE DOCUMENTS。

If you can make fraud recovery 10 times faster, you can not only build a huge commercial empire, but also save billions of dollars for taxpayers。

Make LLMs Easy to Train

DESPITE THE HEAT IN AI, THE EXPERIENCE OF TRAINING LARGE MODELS REMAINS APPALLING。

Developers struggled with broken SDKs every day, spent hours debugging the example of a GPU that just broke down, or found fatal bugs in open source tools. Not to mention the nightmare of processing TB-level data。

Just as the era of cloud computing produced Datadog and Snowflake, the era of AI needed much better & ldquo; shovel & rdquo; We need:

• TOTAL ABSTRACTION OF THE TRAINING PROCESS API。

• The database of mega-data sets can be easily managed。

• Development environment dedicated to machine learning research design。

As “ post-training & rdquo; Post-training and model specialization become increasingly important, these infrastructure will become the cornerstones of future software development。