At its recent AI Ascent 2025 event, Sequoia Capital presented its vision for the next wave of artificial intelligence development. It outlines a future where autonomous AI agents will drive value creation across industries, including agriculture.
The venture capital firm framed the current moment as the largest entrepreneurial opportunity in modern history, comparing it to the early days of cloud computing, but on a far larger scale. While the global software market was worth $400 billion at the start of the cloud era, AI is positioned to capture value from both software and services simultaneously. As a result, startups are scaling faster than ever.
Central to Sequoia's thesis is the idea that most of the value in the AI ecosystem will reside at the application layer-software used directly by businesses and consumers-rather than in the foundational models. This has implications for sectors such as agriculture, where domain-specific tools, co-pilots, and autonomous agents are expected to drive efficiency and improve decision-making at the farm level.
Sequoia urged founders to focus on building AI-native applications for specific workflows, noting that vertical use cases in agriculture and other complex, high-stakes industries could yield significant long-term gains. These tools are not just about automation; they aim to act as full systems capable of executing tasks from end to end. Such systems can evolve from tools to co-pilots and ultimately to autonomous agents.
The event also highlighted the "agent economy"-an emerging system in which AI agents interact with each other, make decisions, and carry out tasks with minimal human intervention. In Sequoia's view, this shift does not replace human work but enhances it, allowing individuals and small teams to operate with vastly more leverage.
Sequoia identified three core technical challenges to making this vision operational: enabling persistent memory for agents, developing communication protocols akin to TCP/IP for inter-agent collaboration, and creating new frameworks for security and trust. These are particularly relevant for agriculture, where AI tools must adapt to diverse environments, interact with machinery, and process unstructured data from the field.
To evaluate AI startups, Sequoia continues to apply traditional investment metrics-such as revenue, margins, and product-market fit-but with added scrutiny on data flywheels, user retention, and adoption behavior. The firm advised founders to avoid "vibe revenue" and instead focus on real, sustainable business metrics.
The rapid acceleration of AI adoption has been driven by global connectivity and low-friction distribution. Tools like ChatGPT launched into an environment with over 5.6 billion people online, compared to just 200 million during the rise of early internet platforms like Salesforce. This infrastructure has allowed new AI products to gain global traction almost instantly.
For agriculture technology startups, the message is clear: moving quickly to develop vertical applications and leveraging proprietary data can lead to competitive moats. Whether through yield optimization platforms, autonomous scouting tools, or AI-powered supply chain systems, the potential for AI to transform agricultural operations is substantial.
Sequoia concluded that while macroeconomic uncertainty persists, the momentum behind AI development is unlikely to slow. The firm encouraged entrepreneurs to adopt a "stochastic mindset"-accepting unpredictability as a feature of building with intelligent systems rather than a flaw. The next wave of innovation, it argued, will belong to those who move fast, build deeply, and solve real problems.
The implications of this AI-driven transformation extend well beyond Silicon Valley. In sectors like agriculture, where high-frequency decisions and fragmented data dominate, the ability to deploy vertical AI solutions could determine which companies capture the next generation of growth.
