The most immediate AI milestone is not singularity, but "Economic AGI," where AI can perform most virtual knowledge work better than humans. This threshold, predicted to arrive within 12-18 months, will trigger massive societal and economic shifts long before a "Terminator"-style superintelligence becomes a reality.

The key to AI's economic disruption is its "task horizon"—how long an agent can work autonomously before failing. This metric is reportedly doubling every 4-7 months. As the horizon extends from minutes (code completion) to hours (module refactoring) and eventually days (full audits), AI agents unlock progressively larger portions of the information work economy.

AI agents can now reliably complete tasks that take a human several hours. With a seven-month doubling time for task complexity, these agents are on track to autonomously handle a full eight-hour workday by the end of 2026, signaling a dramatic shift in the future of work.

Investor Brent Beshore's experience demonstrates a step-function change, not a gradual evolution. His firm's agentic AI projects, which failed after months of effort, were completed in minutes using Claude Cowork just weeks later. This highlights the surprisingly rapid transition of agentic AI from a theoretical concept to a practical, value-creating tool.

The exponential growth in AI agent capabilities creates a plausible scenario where a single entrepreneur can manage a vast array of automated tasks, from development to operations. This raises the possibility of a "solopreneur" achieving a billion-dollar valuation without a traditional human workforce.

Contrary to the view that useful AI agents are a decade away, Andrew Ng asserts that agentic workflows are already solving complex business problems. He cites examples from his portfolio in tariff compliance and legal document processing that would be impossible without current agentic AI systems.

The capability of AI sales agents has accelerated dramatically, with new tools now able to autonomously book six-figure enterprise deals. This rapid pace of improvement indicates that even complex, relationship-driven functions like sales are vulnerable to disruption much faster than anticipated.

The tangible utility of agentic tools like Claude Code has reversed the "AI bubble" fear for many experts. They now believe we are "underbuilt" for the necessary compute. This shift is because agents, unlike simple chatbots, are designed for continuous, long-term tasks, creating a massive, sustained demand for inference that current infrastructure can't support.

The key threat from AI isn't just its capability, but the unprecedented speed of its improvement. Unlike past technological shifts that unfolded over decades, AI agent autonomy on complex tasks has grown exponentially in just two years. This rapid acceleration is what financial systems and labor markets are not stress-tested for.