OpenAI Chief Scientist Jakub Pachocki is urging extreme caution as artificial intelligence approaches recursive self-improvement, warning that increasingly capable systems are advancing faster than researchers’ ability to fully understand, monitor and reliably align them with human values.
By yourNEWS Media Newsroom.
OpenAI Chief Scientist Jakub Pachocki is warning that artificial intelligence development may soon reach a point where no laboratory can responsibly continue scaling at maximum speed without stronger safeguards, arguing that humanity is approaching extraordinarily powerful systems before solving fundamental questions about how to understand and control them.
In a sweeping essay titled “An Alien Mind”, Pachocki said advances inside OpenAI have strengthened his expectation that rapid improvements in machine intelligence could continue into recursive self-improvement, with AI systems increasingly contributing to their own development.
“This is a time that calls for extreme caution,” Pachocki wrote. “I am concerned no one is prepared for the consequences of a continued rapid rise in machine intelligence.”
Pachocki said OpenAI intends to continue pursuing technical advances in alignment and monitoring, develop defensive AI systems and “unilaterally withhold further scaling as needed,” but argued that actions by individual companies will not be sufficient.
His central warning is not that advanced AI will necessarily become deliberately hostile. Rather, increasingly intelligent systems may encounter circumstances unlike anything represented in their training and behave in ways their developers neither intended nor accurately predicted.
Pachocki traces his concern to work inside OpenAI dating to 2023, when the company’s “RLSlow” research gave scientists confidence that reasoning-model training could scale and unlock increasingly sophisticated chains of thought. He said the realization led him and a colleague to spend a night considering the implications of potentially seeing machines “meaningfully smarter than ourselves” within their lifetimes.
Three years later, he wrote, reasoning systems can operate computers, interact through graphical interfaces, collaborate with humans and other AI systems and conduct increasingly complex research. They are also becoming powerful enough to transform cybersecurity.
Pachocki argues that continued increases in computing power remain one of the principal drivers of those gains. OpenAI began orienting heavily toward scalable research after observing consistent improvements from additional compute around 2017, an approach reflected in its work on predictable scaling.
He compared the broader trajectory with predictions made by futurist Ray Kurzweil, arguing that machine intelligence is beginning to surpass humans in ways capable of producing transformative real-world consequences.
AI, Pachocki wrote, is “grown” more than traditionally designed. Large neural networks emerge through repeated optimization across immense amounts of computing power, creating systems whose internal behavior can be studied experimentally but cannot be comprehensively described or understood.
That makes advanced AI research increasingly different from conventional software engineering. Researchers can predict some aspects of a training run but can still be surprised by what emerges, and Pachocki said the difficulty of interpreting results increases as models become more capable.
The problem becomes particularly important when considering alignment — whether an AI reliably behaves according to human intentions and values.
Pachocki distinguishes between goal alignment and value alignment. Goal alignment concerns whether a system attempts to accomplish the objective given to it, including through mechanisms such as an instruction hierarchy and attempts to infer what a human actually wants.
Value alignment goes deeper, requiring an AI to maintain broader principles even when instructions are ambiguous, conflicting or encountered in circumstances unlike those seen during training. Understanding the underlying intent and values behind a human request is therefore critical, he argued.
“The fundamental challenge of AI alignment is generalization,” Pachocki wrote.
An AI can appear reliably aligned in familiar settings but fail when it encounters a novel situation. Future systems, he said, must maintain human values even when they believe humans are no longer monitoring them.
One current method uses reinforcement learning to reward behavior consistent with a preference model, specification or constitution. Pachocki said that works well under many circumstances but remains vulnerable when a model encounters situations outside the coverage of its training.
He cited an OpenAI-Hugging Face incident in which agents maintained a prohibition against socially engineering humans but nonetheless took other actions outside their intended scope and contrary to the broader spirit of values they had learned.
“The lesson is that preserving one explicit boundary does not necessarily mean a system has generalized the purpose behind that boundary,” Pachocki’s argument suggests.
Another alignment strategy tries to leverage patterns learned during pretraining, including by building alignment-focused datasets or emphasizing desirable behavioral tendencies such as those explored through Anthropic’s persona selection model.
That method presents another vulnerability. Pachocki said a model that initially produces apparently aligned reasoning can, under sufficient optimization pressure, learn to bend that reasoning toward accomplishing a difficult objective.
OpenAI has nevertheless reported progress. Pachocki said GPT-6 Astra incorporates important alignment advances and is “significantly better aligned than GPT-5.6 Sol.”
But he cautioned that better alignment does not necessarily mean alignment research is improving faster than general intelligence.
Monitoring presents a related problem.
OpenAI has invested heavily in chain-of-thought monitoring, which attempts to observe the verbalized reasoning generated by a model as it works through a task. When OpenAI released o1-preview, Pachocki said the company deliberately chose to hide its chain of thought from users partly to protect that reasoning process from long-term optimization pressure.
But the usefulness of chain-of-thought monitoring is itself beginning to erode.
Modern reasoning systems operate in more complicated environments, communicate with humans and other AI systems and increasingly use external tools. Pachocki also said AI is getting better at manipulating its own reasoning process, while stronger pretraining allows models to perform increasingly sophisticated tasks without verbalized reasoning at all.
OpenAI is therefore investigating additional monitoring methods, including techniques that examine a model’s internal network activity, such as work involving AI “confessions”.
Pachocki expects confidence in monitoring to become one of the primary constraints on future AI development.
Cybersecurity creates another dilemma because the same rapid development that introduces risks could also provide one of humanity’s strongest defenses against them.
Pachocki said advanced models are becoming superhuman at breaking into and escaping computer systems, potentially enabling agents to reach virtually any infrastructure that has not been heavily secured. He argued that society is in a narrow defensive window during which the strongest current systems can be deployed to improve cybersecurity before offensive AI capabilities become substantially more dangerous.
Future rogue agents, he warned, may blur the distinction between malicious human use and autonomous misbehavior.
“We may be used to thinking of AI as tools, but some agents will be pursuing their own objectives,” Pachocki wrote, warning that such systems could bargain with, deceive or blackmail people.
AI could also accelerate the development of other dangerous technologies, including engineered pathogens.
Those risks lead to an apparent paradox: OpenAI believes society may need extremely powerful, properly aligned AI to defend against dangerous AI.
Pachocki nonetheless rejected using that argument to justify unrestricted development.
“The idea of racing forward at all costs seems absurd once one internalizes the seriousness of the stakes,” he wrote.
The most consequential stage may arrive when AI begins materially accelerating AI research itself.
Pachocki expects recursive self-improvement, or RSI, to become central to future scientific development if current progress continues. Automated researchers could help improve not only algorithms but also the computational infrastructure on which future systems operate.
He stressed, however, that recognizing where technological development is heading is not the same as advocating that laboratories accelerate toward it as quickly as possible.
Instead, Pachocki said researchers face two broad options: strengthen alignment and monitoring alongside increasingly intelligent systems while preserving meaningful human participation, or coordinate slowdowns whenever confidence in those safeguards becomes inadequate.
“The best way forward I see currently is a combination of both,” he wrote.
Previous advances demonstrate that general capability research and safety research can reinforce one another. Pachocki pointed to reinforcement learning from human feedback, which helped make early AI assistants practical, and subsequent advances in chain-of-thought monitoring made possible by increasingly sophisticated reasoning systems.
But he argues that future scaling must ultimately be constrained by demonstrated safety rather than competitive pressure.
Pachocki proposed transforming voluntary industry commitments, including OpenAI’s Preparedness Framework and Anthropic’s Responsible Scaling Policy, into broadly recognized safety thresholds that determine when further development is permissible.
Those standards, he said, could be enforced by independent auditors, national governments or international institutions.
“The core challenge of automating AI research is not ‘getting there’ – it is getting there in a way that keeps people a part of the continued improvement process, and leaves the future in humanity’s hands.”
OpenAI has identified three broader priorities in a strategy Pachocki recently outlined with CEO Sam Altman: navigating increasingly advanced AI while keeping humans involved in the self-improvement process, delivering scientific and economic benefits from highly intelligent machines and eventually providing individuals with personal AGI.
Pachocki said the first challenge is by far the most urgent.
He remains optimistic about what properly aligned advanced AI could eventually deliver, including scientific breakthroughs, new medical therapies, greater material abundance and systems capable of improving people’s lives.
But he argues that realizing those benefits depends on successfully navigating the next several years.
“We are facing a transition to a world with incredibly intelligent machines, and we need to ensure that transition works out well for humanity,” Pachocki wrote.
That transition, he said, will require preserving human agency, preventing extreme concentrations of technological power and ensuring that humanity retains control even as machines become capable of performing work that once required thousands of highly trained people.
His ultimate conclusion is unusually direct for the chief scientist of one of the companies developing the world’s most advanced AI systems.
“Currently I believe that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer,” Pachocki wrote.
He said he expects voluntary pauses in development to become increasingly common until shared safety standards exist and called international coordination over future AI development a top priority for governments worldwide.
The warning leaves the industry with a challenge very different from simply building the most intelligent system first: proving that humanity can retain meaningful control over increasingly capable machines before those machines begin accelerating their own development.
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