OpenAI's team found that as code generation speed approaches real-time, the new constraint is the human capacity to verify correctness. The challenge shifts from creating code to reviewing and testing the massive output to ensure it's bug-free and meets requirements.

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.

While AI excels at writing software—a domain with clear, verifiable outcomes (it compiles or it doesn't)—its application in fields like drug discovery is limited. Progress stalls where outcomes aren't easily and quickly verifiable, requiring complex, expensive, real-world testing.

With AI agents capable of generating code and designs at an unprecedented rate, the new chokepoint in workflows is human review. The primary challenge is no longer production but scaling the evaluation process to ensure AI-generated output aligns with quality standards and company values.

Judgment Labs CEO Alex Shan argues that AI agents will first dominate domains with easily verifiable results, like coding, where a solution's correctness can be quickly checked. Progress will be slower in non-verifiable fields like law or complex drug discovery, where feedback loops are long and ambiguous.

Unlike coding, where context is centralized (IDE, repo) and output is testable, general knowledge work is scattered across apps. AI struggles to synthesize this fragmented context, and it's hard to objectively verify the quality of its output (e.g., a strategy memo), limiting agent effectiveness.

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.

While AI tools transform legal research, they won't replace lawyers. AI's take rate of total legal spend will likely cap at 10-15% because crucial tasks like client counsel and negotiation remain human-centric, unlike the more verifiable and automatable nature of coding.