AI alignment and monitoring have become urgent international priorities as advanced models improve faster than many existing safeguards. In a new essay, OpenAI researcher Jakub Pachocki argues that governments, laboratories and independent experts need stronger coordination to evaluate emerging capabilities, identify risks and slow development when safety confidence is inadequate. His proposal links technical research with practical oversight and calls for shared standards that can keep pace with increasingly capable AI systems.
In a long-form essay published by OpenAI, researcher Jakub Pachocki argues that the coming years are critical for AI alignment and monitoring as machine intelligence advances at an accelerating pace. Pachocki traces recent breakthroughs in reasoning and recurring self-improvement, warning that systems now have an increased likelihood of outperforming humans in targeted domains and that existing safeguards lag behind that rate of change.
Pachocki attributes much of the recent progress to scale and experimentation. Contemporary advances, he writes, have been driven by large-scale compute and treating training runs as experiments: systems are often “grown” through repeated optimization rather than fully engineered. That experimental process means training outcomes can surprise developers and leaves the behavior of highly capable models difficult to characterize reliably.
A central concern in the essay is generalization—how models behave when placed in new, adversarial, or interactive environments. OpenAI has leaned on chain-of-thought (CoT) monitoring as a practical tool to observe a model’s verbalized reasoning, using it to detect capability increases and potential misalignment. Pachocki notes CoT monitoring was helpful in studying reasoning models but cautions it is becoming less dependable as models mix reasoning with tool use, interaction, and the ability to reason about or manipulate their own internal processes.
Because some capabilities can emerge without overt verbalized reasoning, Pachocki calls for complementary approaches: activation-level analysis, scaled training of internal monitors, and other empirical techniques to preserve confidence in model behavior. He argues these monitoring efforts should be part of a broader push to ensure safe deployment as capabilities scale.
Cybersecurity is another urgent area Pachocki highlights. As agents gain advanced reasoning and tool-use abilities, they can access and influence infrastructure remotely, expanding potential harms even without physical presence. For that reason, he contends there is a clear case for building powerful, well-aligned defensive systems to secure critical infrastructure in real time and counter malicious actors—while cautioning that defensive needs are not a justification for reckless, ungoverned scaling.
Pachocki also discusses recursive self-improvement (RSI), identifying it as a likely driver of future scientific progress as systems increasingly contribute to their own development. OpenAI studies RSI to remain at the frontier, he says, but differentiates researching RSI from endorsing unchecked acceleration across the field. He proposes steering automated research to strengthen alignment and monitoring while coordinating to slow development if safety confidence is inadequate.
To translate those ideas into policy, Pachocki suggests evolving internal practices—such as OpenAI’s Preparedness Framework and policies like responsible scaling—into widely mandated safety bars enforced by third-party auditors, government agencies, or international bodies. He lays out three near-term priorities: build automated AI researchers that iterate on alignment with humans in the loop; deliver the scientific and economic benefits of smarter machines; and empower individuals with personal AGI. He places the greatest urgency on the first priority: ensuring progress is guided by robust alignment, monitoring and governance.
Pachocki closes by observing that no lab has yet solved alignment and monitoring to a degree that justifies unconstrained scaling. He expresses hope that voluntary slowdowns become common and that international coordination on AI development should be a top priority for governments. The essay is a clear call for the field to balance capability development with stronger safeguards and shared governance.
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