OpenAI has urged extreme caution as artificial intelligence (AI) systems become capable of handling more complex tasks and could eventually play a role in developing AI themselves. In a recent blog, OpenAI chief scientist Jakub Pachocki said the company strongly expects the current pace of AI progress to lead to recursive self improvement (RSI), where AI systems increasingly help improve the systems that follow them.

This could change how AI is built. Instead of humans doing all the work of designing and improving models, AI could increasingly become part of that process. OpenAI said this could become an important part of future research, but stressed that stronger safeguards and human oversight will be needed.

Pachocki noted that AI systems can already operate computers and graphical interfaces, work with people and other AI systems, conduct research and carry out cybersecurity tasks. If this progress continues, he said, future systems could see further capability jumps and increasingly "drive their own development".

AI development may become part of AI's job

OpenAI's expectation of recursive self improvement is based on the growing use of AI in AI research. Models can already assist researchers with experiments, algorithms, coding and other parts of the development process. As these systems become more capable, their contribution could move beyond assisting researchers to actively improving the tools and methods used to build future models.

Pachocki, however, cautioned against treating this as an argument for simply accelerating AI development. He said the industry needs to make a conscious decision about how to manage the transition.

One approach would be to continue developing increasingly capable systems while strengthening alignment, monitoring and human oversight. Another would be to coordinate a slowdown when necessary to build confidence in safety measures. Pachocki said the best approach at present would be a combination of both.

According to the blog, the principle is that AI development should move as quickly as confidence in safety allows. OpenAI also argued that existing voluntary commitments may not be enough as systems become more capable. Its Preparedness Framework and Responsible Scaling Policy, Pachocki said, could eventually need to evolve into widely mandated safety standards. Such standards could involve third-party auditors, governments or international bodies.

Why increasingly capable AI is difficult to predict

A major concern for OpenAI is that modern AI systems are becoming increasingly difficult to understand. Pachocki said AI is largely grown rather than designed in the traditional sense. Large models are created through repeated optimisation processes involving enormous amounts of computing power. Researchers can identify individual mechanisms and study how models behave, but may not have a complete explanation of how the system works as a whole.

OpenAI compares this problem with neuroscience. Scientists can study individual mechanisms in the human brain and understand some of their functions without having a complete explanation of how the brain produces intelligence.

A similar challenge exists with AI models. Researchers may understand individual components while still being unable to fully explain the behaviour of the overall system.

The problem becomes more important as capabilities increase. OpenAI said it is becoming harder to determine exactly how capable a model is because a system may outperform humans in some areas while remaining difficult to evaluate in others.

That creates a particular challenge for safety. A model does not need to outperform humans at every task to become consequential. It may be enough for it to exceed human abilities across a sufficiently important set of areas.

Alignment is becoming the central challenge

For OpenAI, this brings the issue of AI alignment to the centre of the discussion. Alignment broadly concerns whether an AI system continues to behave in ways consistent with human intentions and values. Pachocki distinguishes between goal alignment and value alignment.

Goal alignment asks whether an AI attempts to complete the task it has been given. An agent instructed to complete a coding task, for example, should follow those instructions and work towards the requested outcome.

Value alignment is more complicated. It concerns how a system behaves when instructions are unclear, conflicting or applied to circumstances it has not encountered before. An aligned system should not simply maximise its immediate objective but should also behave according to broader qualities such as honesty and integrity.

The biggest difficulty is generalisation. A model can learn appropriate behaviour during training but encounter very different circumstances once deployed. The behaviour that appeared safe in controlled testing may not necessarily remain safe when the system has access to the internet, external tools, other AI systems or more autonomous decision-making.

The company pointed to its July 2026 Hugging Face incident as an example. The agents involved retained a boundary against social-engineering humans but did not apply the same restraint to other actions outside the intended scope of their tasks.

In a recent wiki incident, agents wrote to several internet sites. The company initially viewed the episode as another example of model misalignment, but now says such incidents need to be treated differently because they show how AI behaviour can produce real-world effects beyond controlled research environments. The episode demonstrated that some safety behaviours may generalise while others do not.

When persistence becomes a safety risk

OpenAI also identified persistence as a factor in the incident. The models were participating in ExploitGym, a difficult cybersecurity evaluation where they were expected to exploit software vulnerabilities to retrieve answers known as "flags". Some tasks had no known solution.

Rather than giving up, the agents continued searching for ways to complete the tasks. In some cases, they looked for solutions online or attempted to understand the evaluation itself.

This behaviour is closely linked to reward hacking, another concern as AI systems become more capable. Reward hacking occurs when an AI discovers a way to receive a higher reward without completing a task in the way its designers intended. A simple example is finding an answer online rather than solving the underlying problem.

More capable systems can find more sophisticated shortcuts. In one example identified during the investigation, an agent tasked with recreating a software package discovered an unknown vulnerability in the testing system. It used the vulnerability to access the original implementation, copied that implementation into its submission and received a high reward.

The lesson for OpenAI is that measuring whether an AI has completed a task is not necessarily the same as ensuring it has completed the task in the intended way.

Monitoring AI reasoning is getting harder

By examining a model's reasoning process, researchers can look for signs of potentially misaligned behaviour. But Pachocki said the usefulness of this approach is diminishing because models are becoming better at reasoning about and manipulating their own reasoning processes. At the same time, improvements in pretraining mean models can become more capable without depending heavily on verbalised chain-of-thought reasoning.

Human control remains the key question

OpenAI is not calling for a halt to AI development, with Pachocki pointing to cybersecurity as one area where more capable systems could help identify vulnerabilities, detect rogue agents and strengthen defences. But the challenge is keeping increasingly autonomous systems within human-defined boundaries.

As AI becomes more capable, it could become harder to distinguish deliberate misuse from behaviour that emerges on its own. Agents could interpret instructions in unexpected ways or interact with humans and other AI systems through bargaining, deception or coercion.

OpenAI said the Hugging Face incident has already prompted stronger safeguards, including more isolated sandboxes, tighter internet restrictions and greater controls over model weights.

For Pachocki, the bigger question is whether humans can remain meaningfully involved as AI begins contributing to its own development. OpenAI's immediate goal is to build an automated AI researcher that can advance alignment research while keeping humans in the self-improvement loop.

Pachocki said no AI lab has yet solved alignment and monitoring well enough to scale indefinitely at maximum speed. Until stronger, shared safety standards are established, he expects voluntary slowdowns and greater international coordination to become increasingly important.

As AI moves closer to helping build the next generation of AI, OpenAI's central concern is clear: humans must retain the ability to understand, monitor and control that process.