Pre-reasoning AI models were static assets that depreciated quickly. The advent of reasoning allows models to learn from user interactions, re-establishing the classic internet flywheel: more usage generates data that improves the product, which attracts more users. This creates a powerful, compounding advantage for the leading labs.

Marc Andreessen frames the current AI progress as the culmination of eight decades of research, finally unlocked by the proven success of neural networks. What seems sudden is actually the payoff of a long, often controversial, scientific journey.

According to Sequoia's Pat Grady, the best time to start an AI application company is now. The foundational playbook has been established through three key technological leaps: pre-training (ChatGPT), reasoning (01), and long-horizon agency (Claude). This clarity provides a stable platform for building valuable applications.

Marc Andreessen frames today's AI advancements not as a sudden event but as the payoff from eight decades of foundational research. This long view contextualizes the rapid progress and suggests its stability compared to past AI summers and winters.

While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.

Unlike any prior tool, AI can be directly applied to improve its own creation. It designs more efficient computer chips, writes better training code, and automates research, creating a recursive self-improvement loop that rapidly outpaces human oversight and control.

Companies like OpenAI and Anthropic are not just building better models; their strategic goal is an "automated AI researcher." The ability for an AI to accelerate its own development is viewed as the key to getting so far ahead that no competitor can catch up.

Andreessen presents the modern AI agent's architecture—a language model combined with a Unix shell and file system—as a major software breakthrough. This modular, extensible design mirrors the powerful Unix mindset, enabling agents that are independent of specific models and can modify themselves.

The current wave of AI, particularly agentic technology, is not just another incremental improvement. It's a confluence of major technological shifts, enabling automation at a rate of 5-10% per week, leading to exponential increases in productivity that dwarf prior innovations like cloud or mobile.