Long Article Introduction: Strategic Advice from Sequoia Capital for Entrepreneurs: How AI Can Become the Next Trillion-Dollar Economy?

At the AI Ascent 2025 conference hosted by Sequoia Capital (, Sequoia partners Packer Radio, Sonya Huang, and Konstantine Buhler delved into the latest trends, business potential, and entrepreneurial guidance in the AI industry. They emphasized that AI is no longer just a vision of the future but a reality with unprecedented scale and enormous opportunities.

Why is AI important? How will it comprehensively reshape the economy and work patterns?

AI Market Exceeds Cloud: A Major Industrial Transformation Crossing Software and Labor

Packer Radio first paid tribute to the classic framework proposed by Sequoia Capital founder Don Valentine, starting with questions such as "What is AI? What impact does it bring? How should we respond?"

He predicts that the market size at the starting point of AI transformation will be ten times larger than that of the software era of cloud transformation. The market development in the next 10 to 20 years will be "absolutely enormous" and will simultaneously impact the profit markets of "services" and "software":

From tools evolving into "co-pilot )co-pilot(" and even "autopilot )autopilot(", AI will not only reshape services but also disrupt the entire software industry and labor market.

Additionally, it was mentioned that "the two overall target markets )TAMs( have shifted from selling tools to selling results and are now open for competition."

) Microsoft CEO Nadella: Majorana 1 drives breakthroughs in quantum computing, cloud services become the biggest winners in the AI industry (

The next wave created by AI: the whole world is already on board.

Packer reminds the world that since the moment ChatGPT came out, AI has fully entered the practical stage. An epoch-making technology requires computing power, network coverage, data scale, and user base, and these are already in place:

Mainstream narratives of each era

Given that the necessary elements of this revolution have been constructed, when the starting gun fires, the adoption of AI will face almost no barriers, making it the fastest and most widespread case of technology dissemination in history.

How Startups Can Win the AI Race: The Application Layer Becomes the Value High Ground

Packer's chart indicates that the most successful technology companies in history have almost all come from the application layer )Apps(, and the same will be true in the AI era, where true value will accumulate in the hands of startups that understand how to start from the "customer perspective":

White space: The application layer is still an underdeveloped area with great potential for innovation.

The vertical strategy that focuses on specific industries or functions to solve problems, and establishes competitive barriers through human-machine collaboration, is currently the most promising competition track.

He cited examples: "The first batch of AI's 'killer applications' has already emerged, including ChatGPT, Harvey, Glean, Sierra, Cursor, and A Bridge."

Sonya specifically mentioned the deep applications in areas such as "medical diagnosis, voice assistants, educational concepts, and advertising visualization," providing entrepreneurs with some directions.

Sequoia's AI Investment Guide: Four Filters from Revenue Flywheel to Technical Strength

When selecting investment targets related to AI, Sequoia emphasizes that 95% of the criteria are the same as in other industries, but the unique 5% of AI focuses on four key indicators:

Substantiality of revenue: Distinguish between real business growth and companies that are only experiencing short-term "sampling"; focus on product adoption rates, user engagement, and retention rates.

Customer Trust: At this stage, trust in the company is more important than the product itself; trust is the foundation of long-term success.

Potential gross margin improvement capability: As AI computing costs decrease, companies need to demonstrate concrete practices that can ensure a healthy gross margin.

Effective Data Flywheel: The data flywheel must be directly related to business metrics; otherwise, it cannot form effective competitive barriers.

These are moats that cannot be easily replicated, and will serve as important criteria for determining whether an AI startup has long-term competitive strength.

Towards the Era of AI Agency Economy: AI is no longer just a tool, but a digital partner

Konstantine subsequently revealed that the training of large models is slowing down, and is instead looking for new breakthroughs, including OpenAI's "reasoning ability )reasoning(" and Anthropic's "model context protocol )MCP(", all of which are trying to create new methods to expand applications.

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Among them, the next major wave of AI will be the "agent economy":

AI technology is evolving from a single-task executor to a "agent )agent(" that can collaborate, communicate, and transact. In the future, we will enter a true "agent economy )agent economy(," where these intelligent agents will work alongside humans to create a highly automated and trusted digital economy.

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The three major technical barriers of agency economy: identity, communication, and security.

To truly realize the agent economy, AI must also overcome three major technical hurdles, which will all be new battlegrounds for technological innovation in the future:

Establish a stable and lasting identity system )persistent identity(: Agents themselves need to maintain a consistent personality and understanding, and continuously understand users. There are still significant challenges in memory and self-learning.

Developing a seamless communication protocol layer between agents )seamless communication protocols(: Agents need to establish a TCP/IP protocol layer similar to personal computers to transmit information and value.

Build a trust-based security mechanism )security(: In cases where the trading parties are all agents, the importance of security and trust needs to be enhanced, which is expected to give rise to a cybersecurity industry centered around trust and security.

) From financial advisors to secretaries, the trust challenges of AI agents: Can we trust the autonomous decisions of artificial intelligence? (

Redefining Work and Economy: The New Era of "High Leverage and High Randomness" Brought by AI

Konstantine concluded that AI will not only reshape work patterns but also change people's thinking patterns, shifting from the "deterministic" of traditional computer science to a "stochastic mindset )", after all, AI, like humans, has unpredictable memory and responses.

The same applies at the management level, managing AI agents requires making more complex decisions, including blocking and feedback of processes, which is also a discipline.

It is not hard to imagine that the future will be an era of "a few driving the production of many" with high leverage. We have already seen many companies expand at an unprecedented speed with fewer personnel. We will be able to do more, but we must also manage the uncertainties and risks involved.

Ultimately, these processes will merge with AI agents to form a vast neural network, reshaping personal work, transforming businesses, and even redefining the economy, thus no one will be excluded.

This article provides a long-form guide on Sequoia Capital's strategic advice for entrepreneurs: How AI can become the next trillion-dollar economy? Originally appeared in Chain News ABMedia.