Chainfeeds Summary:
Here are 10 core sectors that Y Combinator is closely monitoring and eager to invest in.
Article source:
https://x.com/chuhaiqu/status/2020334549021126916
Article Author:
Going overseas to incubators
Opinion:
Going Global to Incubators: In the past few years, tools like Cursor and Claude Code have revolutionized the way we write code. However, this boom has masked a more fundamental problem: writing code is just a means; the real challenge lies in figuring out "what to build." Currently, product discovery remains in a very rudimentary stage, relying on fragmented user interviews, difficult-to-quantify market feedback, and countless scattered Jira tickets. This process is highly manual, suffers from severe information gaps, and decisions are often based on experience and intuition. 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 customer interview recordings, product usage data, and feedback records, then directly ask us what the next step should be. The system can not only provide clear suggestions for feature priorities but also output a complete feature outline based on real user needs, using concrete data and feedback to justify the rationale behind each decision. Furthermore, it can even automatically generate UI prototypes, adjust data model structures, and break down requirements into actionable tasks for an AI Coding Agent to complete. As AI gradually takes over code implementation, the ability to "define products" will become more important than ever, and establishing a complete closed loop from requirement discovery to product definition will become the most critical competitive advantage for future product teams. The next wave of real opportunities may well come from the emergence of AI-native hedge funds. This is not simply about embedding AI as an auxiliary tool into existing quantitative strategies, but about building an AI-driven investment system from scratch. In the 1980s, when the first funds attempted to use computers to analyze the market, Wall Street was skeptical, but today quantitative trading is standard practice. We are experiencing a similar turning point. Future hedge funds may be operated collaboratively by a large number of AI agents that can read 10-K financial statements, listen to conference calls, analyze SEC documents, and synthesize analyst opinions to make trading decisions 24/7. Compared to traditional institutions, these systems can process information faster and more comprehensively, and continuously optimize strategies. While existing quantitative giants have abundant resources, they are slow to act between compliance and innovation, while the new generation of players is more flexible. In this field, the real alpha may come from teams willing to allow AI to deeply participate in and even dominate investment decisions. This model will not only change the way transactions are conducted, but may also redefine the role of fund managers themselves, shifting from judgments based on human experience to information processing and execution systems centered on machines. AI is also fundamentally transforming the business models of service companies. In the past, design firms, advertising agencies, and law firms all faced a common bottleneck: difficulty in scaling, because they were essentially selling human time. Profit margins were limited, growth depended on recruitment, and efficiency was difficult to improve. AI is breaking down this structural constraint. The next generation of agencies will no longer simply sell tools or consulting, but will leverage AI to produce results a hundred times more efficiently and deliver the final product directly to clients. For example, design firms can use AI to generate complete customized solutions before signing contracts, suppressing traditional competitors with lower costs and higher speed; advertising agencies can generate cinematic video ads without expensive filming; law firms can draft complex legal documents in minutes instead of weeks. Future service companies will resemble software companies in their business models: higher gross margins, greater scalability, and lower marginal costs. Their core assets will no longer be the number of employees, but rather automation capabilities and AI workflows. This means that traditional industries that rely on sheer numbers and manpower may undergo a profound restructuring in the coming years.
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