While AI agents seem to create infinite intelligence, they reveal more fundamental constraints. The real limits are no longer human time, but the finite capacity of markets to absorb outputs, the hard financial cost of tokens and compute, and the human ability to provide effective judgment and evaluation.

Historically, time and cost acted as a natural defense against overwhelming systems. AI agents can now execute millions of tasks—like filing legal motions or making lowball offers—for nearly free, threatening to collapse systems not built for this scale.

Processes like grant writing and college admissions rely on formulaic, 'bullshit work' that AI excels at. The inevitable flood of AI-generated 'slop' applications will make human review untenable, forcing these legacy systems to either fundamentally reform their evaluation criteria or collapse under the volume.

Contrary to the belief that AI will boost government efficiency, citizens are using tools like ChatGPT to navigate complex bureaucracies. This creates a surge in well-formed claims and appeals, overwhelming systems like employment tribunals and planning departments, effectively slowing them down.

Historically, well-structured writing served as a reliable signal that the author had invested time in research and deep thinking. Economist Bernd Hobart notes that because AI can generate coherent text without underlying comprehension, this signal is lost. This forces us to find new, more reliable ways to assess a person's actual knowledge and wisdom.

Systems like the legal and tax systems assume human-level effort, making them vulnerable to denial-of-service attacks from AI. An AI can generate millions of lawsuits or tax filings, overwhelming the infrastructure. Society must redesign these foundational systems with the assumption that they will face persistent, large-scale, intelligent attacks.

The true exponential acceleration towards AGI is currently limited by a human bottleneck: our speed at prompting AI and, more importantly, our capacity to manually validate its work. The hockey stick growth will only begin when AI can reliably validate its own output, closing the productivity loop.

Advanced AI tools like "deep research" models can produce vast amounts of information, like 30-page reports, in minutes. This creates a new productivity paradox: the AI's output capacity far exceeds a human's finite ability to verify sources, apply critical thought, and transform the raw output into authentic, usable insights.

The ease of generating legal content with AI will inundate the system with documents and contracts. This creates a bottleneck, increasing the need for actual, human lawyers to review, approve, and manage this massive new volume of work.