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.

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A consortium including leaders from Google and DeepMind has defined AGI as matching the cognitive versatility of a "well-educated adult" across 10 domains. This new framework moves beyond abstract debate, showing a concrete 30-point leap in AGI score from GPT-4 (27%) to a projected GPT-5 (57%).

Julian Schrittwieser, a key researcher from Anthropic and formerly Google DeepMind, forecasts that extrapolating current AI progress suggests models will achieve full-day autonomy and match human experts across many industries by mid-2026. This timeline is much shorter than many anticipate.

Silicon Valley insiders, including former Google CEO Eric Schmidt, believe AI capable of improving itself without human instruction is just 2-4 years away. This shift in focus from the abstract concept of superintelligence to a specific research goal signals an imminent acceleration in AI capabilities and associated risks.

The economic incentive for VCs funding AI is replacing human labor, a $13 trillion market in the US alone. This dwarfs the $300 billion SaaS market, revealing the ultimate goal is automating knowledge work, not just building software.

OpenAI's new GDPVal framework evaluates AI on real-world knowledge work. It found frontier models produce work rated equal to or better than human experts nearly 50% of the time, while being 100 times faster and cheaper. This provides a direct measure of impending economic transformation.

OpenAI is launching initiatives to certify millions of workers for an AI-driven economy. However, their core mission is to build artificial general intelligence (AGI) designed to outperform humans, creating a paradox where they are both the cause of and a proposed solution to job displacement.

The narrative of AI destroying jobs misses a key point: AI allows companies to 'hire software for a dollar' for tasks that were never economical to assign to humans. This will unlock new services and expand the economy, creating demand in areas that previously didn't exist.

A useful mental model for AGI is child development. Just as a child can be left unsupervised for progressively longer periods, AI agents are seeing their autonomous runtimes increase. AGI arrives when it becomes economically profitable to let an AI work continuously without supervision, much like an independent adult.

The enormous market caps of leading AI companies can only be justified by finding trillions of dollars in efficiencies. This translates directly into a required labor destruction of roughly 10 million jobs, or 12.5% of the vulnerable workforce, suggesting market turmoil or mass unemployment is inevitable.

The real inflection point for widespread job displacement will be when businesses decide to hire an AI agent over a human for a full-time role. Current job losses are from human efficiency gains, not agent-based replacement, which is a critical distinction for future workforce planning.