The primary obstacle for tools like OpenAI's Atlas isn't technical capability but the user's workload. The time, effort, and security risk required to verify an AI agent's autonomous actions often exceed the time it would take for a human to perform the task themselves, limiting practical use cases.

Beyond model capabilities and process integration, a key challenge in deploying AI is the "verification bottleneck." This new layer of work requires humans to review edge cases and ensure final accuracy, creating a need for entirely new quality assurance processes that didn't exist before.

Andrej Karpathy's 'Software 2.0' framework posits that AI automates tasks that are easily *verifiable*. This explains the 'jagged frontier' of AI progress: fields like math and code, where correctness is verifiable, advance rapidly. In contrast, creative and strategic tasks, where success is subjective and hard to verify, lag significantly behind.

When AI can generate code and designs endlessly, creating "AI slop," the critical human contribution becomes judgment. The key challenge shifts from *building* to *deciding what to build* and evaluating the output's quality and security. The question is no longer "can we build it?" but "should we build it?"

Simply deploying AI to write code faster doesn't increase end-to-end velocity. It creates a new bottleneck where human engineers are overwhelmed with reviewing a flood of AI-generated code. To truly benefit, companies must also automate verification and validation processes.

AI can produce scientific claims and codebases thousands of times faster than humans. However, the meticulous work of validating these outputs remains a human task. This growing gap between generation and verification could create a backlog of unproven ideas, slowing true scientific advancement.

Don't blindly trust AI. The correct mental model is to view it as a super-smart intern fresh out of school. It has vast knowledge but no real-world experience, so its work requires constant verification, code reviews, and a human-in-the-loop process to catch errors.

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.