Alliance's Co-Founder's Letter to Entrepreneurs: Written on the Occasion of Cursor's $60 Billion Sale

链捕手Published on 2026-06-20Last updated on 2026-06-20

Abstract

In this letter to entrepreneurs, Alliance reflects on the success of Cursor's $60 billion sale to Elon Musk, using it as a case study to counter the misconception that opportunities in crowded fields like AI or crypto are exhausted. The piece argues that great companies like Cursor, Stripe, Figma, and Shopify are not built by geniuses with perfect ideas, but by founders who start with a non-consensus belief about the future and build for years before that future becomes obvious to everyone. They identify long-term shifts, find overlooked entry points, and execute relentlessly. The framework for success involves: 1. **Identifying your place in the technology cycle**: Early-stage opportunities focus on making new tech usable for power users (e.g., Coinbase, Cursor). Later-stage opportunities involve finding the "yin" to an existing "yang"—the blind spots of first-generation players (e.g., Stripe vs. PayPal, Figma vs. Adobe). 2. **Cultivating unique insights**: Immerse yourself deeply in the market. Use every product, talk to users, and build an audience. Insights will emerge naturally from deep engagement. 3. **Finding a "hair-on-fire" problem**: Look for a 10x improvement or a severe, urgent pain point. The strongest signal is people already building clumsy workarounds. 4. **Building a focused MVP**: Don't just add features because you can. Ask why users would abandon their current tool for yours. The best startups rarely force new behaviors; they improve familiar workfl...

Author:Imran

Compiled by:Jiahuan, ChainCatcher

Sitting at your computer, an idea for a startup is born. You see Cursor being sold to Elon Musk for $60 billion. Perhaps the idols of the previous generation were Mark Zuckerberg or Evan Spiegel. You look at these founders and can't help but compare yourself to them. They don't seem much smarter than you. Their resumes aren't more impressive than yours.

So naturally, you ask yourself: Why can't I do the same thing? This is where most founders begin their journey. But this is also where most founders get stuck.

They see Artificial Intelligence. They see Cryptocurrency. They see thousands of startups that have already secured funding. Every field seems crowded. Every obvious idea has already been tried.

They conclude: The opportunities are gone.

So they close their laptop, give up, and walk away.

This is how a large portion of startups die before they are born. Not because the founders lack capability, but because they think the game is already over.

Let's take Cursor as an example. Not every path is a direct and smooth one.

As early as 2022, Cursor began its arduous "glass-chewing" phase. That was even before ChatGPT was born. There was no ready-made playbook to follow. No obvious market. Only a belief: AI would fundamentally transform knowledge work.

To stay grounded, they focused on three things. First, they chose an area they were genuinely excited about: Artificial Intelligence. Second, they became customers of their own product. Third, they focused unwaveringly on power users.

Because if you can win over power users, winning over everyone else becomes easy. Frankly, this story is not unique to Cursor.

When Stripe started, the online payment problem seemed solved, but the founders believed developers would increasingly become decision-makers within companies, and whoever won the developers would ultimately win the internet. They had personally experienced this pain point. Even though PayPal had proven online payments worked, Stripe saw an opportunity to build a "developer-first" version of the future.

Figma spent years developing before the market was ready because they believed the future of design wasn't about a better single design tool, but about collaborative design with everyone working in the same file. Google Docs had already demonstrated the power of real-time collaboration for documents. Figma extended this insight to design.

Shopify started out just to sell snowboards online because the founders believed millions of small businesses yearned to have their own customers, brands, and destinies, rather than depending on large platforms. Amazon had proven centralized e-commerce worked. Shopify bet that entrepreneurs would eventually want to take control.

Different products. Same pattern.

Every founder started with a non-consensus belief about where the world was going, then spent years quietly building before that future became obvious to everyone. Their luck was riding on strong tailwinds.

For Stripe, this wind was the conviction that more and more commerce would move online. For Figma, this was the belief that software would be cloud-first and collaborative by default. For Shopify, this was the hope that the internet would empower millions of entrepreneurs to build independent businesses.

Cursor followed a similar trajectory. The company was built on the belief that AI would fundamentally reshape knowledge work, and software engineers would be the first power users to adopt it. Today the product seems obvious, but when they started, there was no clear roadmap. Only belief.

Different products. Different markets. Same underlying logic.

Identify long-term trend shifts early, find the entry points others miss, spend years executing before the rest of the market catches up. Every Yang has its Yin. PayPal spawned Stripe. Adobe spawned Figma. Amazon spawned Shopify.

The first generation proves the market exists. The second generation rebuilds it around new insights, new technologies, or shifting customer behaviors. For founders, the important question is to figure out where you are in the cycle. If you're entering early, like Coinbase or Cursor, your opportunity often lies in making the new technology practically usable for power users.

Coinbase didn't invent cryptocurrency. It just made buying and holding Bitcoin incredibly simple, far better than managing your own wallet or wiring money to Mt. Gox.

Cursor didn't invent AI programming. It simply realized that autocomplete wasn't the endgame; what developers truly craved was an AI-native way of developing software.

But if you're entering mid-to-late cycle in a tech shift, opportunity usually looks different. Infrastructure exists. The market is proven. Your job is not to prove if the technology works, but to find the "Yin" for the existing "Yang," the blind spot overlooked by the first players. Many of the greatest companies are born here.

Now you've identified where you are in the tech shift. You have a few ideas and are ready to go, but then you realize something unsettling: You don't actually have many unique insights. You don't have a deep understanding of the market, the customers, or even the product. And that's perfectly normal.

This is when you must roll up your sleeves and start building relationships, insights, and reputation. Fortunately, we live in an era with X (Twitter), making this easier than ever. You can build an audience, meet customers, engage with power users, and learn directly from those shaping the market.

The first thing I would do is experience every product in the field. If you're starting a company in a sector but aren't a power user of the benchmark products, it's hard to develop unique insights about where the market is going. Map out every product in the ecosystem. Become a power user of each one. Talk to people who love them, hate them, and have abandoned them. Understand why they stayed, why they left, and the features they wish existed but don't.

Eventually, you'll discover that most markets aren't won because incumbents are stupid. They get replaced precisely because they became successful.

As companies grow, they naturally drift away from individual users. Feedback cycles lengthen, edge cases are ignored, and a new generation of power users emerges that doesn't fit the existing product. This is where sharp founders spot opportunities.

The goal isn't to brainstorm an idea in isolation. The goal is to immerse yourself so deeply in the market that the missing piece becomes obvious. Once you do this long enough, you'll stop hunting for ideas and start noticing them everywhere. This is precisely the state you want to reach. Ultimately, you'll find there are more opportunities than you can possibly tackle.

Next comes the hard part: Choosing one.

Once you've settled on what you think is the right idea, the next question is simple: Is this a 10x improvement, or a hair-on-fire problem? If the answer is no, don't bother. People rarely switch products for incremental improvements. They switch when something is significantly better or when the pain point is severe enough that it demands an immediate solution.

The easiest way to find hair-on-fire problems is to look for people already cobbling together workarounds. Spreadsheets, WhatsApp groups, cumbersome manual processes, copying data between systems – these are all signals.

The best founders look for pain points because when the pain is great enough, customers can't wait to rip the product out of your hands. And when the pain is minor, no amount of marketing, growth hacking, or clever positioning will save you.

Now you've confirmed the idea, found the pain point, and are building the MVP.

With Claude, Codex, and various AI tools, building a product has never been easier. Ironically, this also becomes its own trap.

I find myself adding feature after feature simply because "I can." The product slowly becomes a Frankenstein-like monstrosity. Each feature seems reasonable in isolation, but together, they make the product worse.

Ultimately, I return to first principles. The most important question isn't what feature I should build. It's why would someone abandon their existing tool and switch to yours?

Every great startup has an answer to this question. Cursor could have built yet another programming plugin. Instead, they forked VS Code. Developers already loved this editor, understood how it worked, and had it embedded in their daily workflows.

Cursor didn't ask users to learn something entirely new. It simply let users keep doing what they already loved, just with AI fused directly into the experience.

The best startups rarely force users to learn entirely new behaviors. Instead, they find familiar workflows, remove friction, and make them significantly better.

As founders, we're obsessed with what we're building. Customers care about what they have to give up. The lower the switching cost and the higher the value created, the faster the adoption. This is why the best MVPs aren't feature-rich. They are intensely focused on giving customers a single compelling reason to switch.

At this point, you've found the pain point, built the MVP, and hopefully given customers a strong reason to choose you. Next comes the part most founders underestimate: Distribution channels.

I've seen founders spend months grinding on a product and only five minutes thinking about how users will find it. The truth is, distribution channels are often the moat.

Airbnb didn't win because its website was better. The founders knocked on doors, personally photographed apartments, and manually onboarded landlords city by city. Stripe recruited developers one by one. Coinbase was active in Bitcoin forums long before crypto went mainstream.

Cursor is another excellent example. Their team posted on Hacker News six times. Most posts got no traction. They sent DMs to thousands of developers, listened to feedback with extreme patience, and won users one by one.

Today everyone says Cursor's success was inevitable. But for years, they were doing unscalable, manual work.

Founders love to talk about product-market fit, but before achieving that, you first need distribution-market fit. Where do your customers spend their time? Who do they trust? How do they discover new products? The best founders don't just build products. They build distribution engines. Because the market can't fall in love with a product it never sees.

The final stage in all of this is resilience, adaptability, and never giving up.

Unfortunately, I can't teach you this. No one can. It can only be experienced.

Cursor is again an excellent case study. They spent years developing before the market was mature. They posted repeatedly, DMed thousands of users, and were ignored by most. In hindsight, it all makes sense. At the time, the future was uncertain.

The same pattern is everywhere.

Airbnb's founders faced rejection after rejection, even resorting to selling cereal boxes to keep the company afloat.

Nvidia faced multiple near-death experiences before becoming one of the world's most valuable companies.

Rain, a startup in our incubation batch, was born after the FTX collapse, when most thought crypto was dead. While others fled the industry, they kept building. A few years later, they raised over $100 million at a $2 billion valuation.

The lesson isn't that these founders are smarter than you. It's that they stayed in the game long enough for their insights to compound.

So, I've laid out the entire framework for you.

Look for shifts in technology cycles. Cultivate unique insights. Obsess over your market. Talk to customers. Find hair-on-fire problems. Build the simplest possible entry point. Win your distribution channel.

Most importantly, when things get tough, absolutely do not give up.

That's it.

There's no secret. Most people can't do these things consistently over the long term. The few who do end up building the great companies that the next generation of founders study.

The world is yours.

Go build.

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Related Questions

QWhat is the primary message of this open letter to entrepreneurs from Alliance?

AThe core message is that great opportunities are not gone, even in seemingly crowded fields. Founders fail not from lack of capability, but from believing the game is over. True success comes from identifying long-term trends, developing unique insights, deeply immersing in the market, solving critical 'hair-on-fire' problems, building a simple MVP, mastering distribution, and persisting through immense challenges.

QAccording to the letter, what common pattern do successful companies like Cursor, Stripe, and Figma share in their early days?

AThey were all founded on a non-consensus belief about the direction of the world. They focused on a deeply exciting area, were their own customers, and served power users. They spent years building in obscurity before their vision became obvious to everyone, riding a powerful generational tailwind of technological or behavioral change.

QHow does the author differentiate the opportunities for founders entering early in a technology shift versus those entering later?

AFor early entrants (like Coinbase or Cursor), the opportunity typically lies in making the new technology genuinely usable for power users. For later entrants, the infrastructure and market are proven. The opportunity shifts to finding the 'yin' to the existing 'yang' – the blind spots and new needs ignored by the first-generation players who have become successful and distant from the edge cases.

QWhat is the 'first principle' question a founder should ask about their MVP, as highlighted in the article?

AThe most important question is not what features to build, but 'Why would someone switch from their existing tool to yours?' The best startups provide a compelling, often ten-times-better reason to switch by eliminating friction in a familiar workflow, rather than forcing users to learn entirely new behaviors.

QWhat crucial element do many founders underestimate, which the author argues is often the real moat?

ADistribution. Many founders spend months on the product but only minutes thinking about how users will discover it. The author argues that distribution is often the real moat. Founders must achieve 'distribution-market fit' before product-market fit by manually building a distribution engine, going where their customers are, and earning trust one user at a time through unscalable, hard work.

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Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

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What is AGENT S

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