Sam Altman said during an investor livestream in October 2025 that OpenAI is “tracking toward achieving an intern-level research assistant by September 2026 and a fully automated ‘legitimate AI researcher’ by 2028.” This roadmap toward fully autonomous AI researchers signals a fundamental shift in how knowledge work gets done, one where human value centers on direction, judgment, and verification rather than execution.
For HR leaders, this means the next few years require deliberate action: redesigning job architectures to distinguish between orchestrating work and producing it, evolving performance systems to reward reasoning quality over output volume, and refining hiring practices to prioritize problem-framing and cross-functional judgment alongside technical skill
And before you think the timelines seem rushed, consider this: the length of software engineering tasks are doublingevery four months. OpenAI’s latest release in November 2025, GPT‑5.1-Codex-Max, can do fully autonomous AI coding for a full 24 hours on its own.
How does knowledge work change when AI can handle research?
A model that can run multi-step reasoning for hours creates a new pattern. Human teams focus more attention on defining the problem, specifying constraints, and reviewing outputs. The deeper pattern we are seeing is that direction and judgment take precedence over execution. Teams evolve from producing individual tasks toward orchestrating sequences of work that include automated components.
What does this look like day to day? A researcher or analyst may hand a problem to an autonomous agent with detailed context. They then evaluate the resulting paths, test underlying assumptions, and guide the next iteration. CHROs will be asked to help teams understand how to structure that collaboration. Policies for access, oversight, and documentation become part of role expectations. This introduces a need for clearer job architecture, it also creates openings for new competency models that distinguish between producing work and shaping the work.
The point that often gets missed is that this shift rewards employees who can move fluidly between abstract reasoning and practical evaluation. These skills help teams maintain quality and avoid over-reliance on any single step of automation. Early guidance from HR helps teams build confidence in these new patterns.
How does performance management change when execution becomes more automated?
Performance systems will also need to evolve. When autonomous agents execute extended sequences of work, human contribution centers on clarity of direction, strength of verification, and quality of interpretation. Volume becomes less meaningful as an indicator of excellence. The value shifts toward thinking, checking, and deciding.
A practical way to frame this is to break contribution into three layers.
- Direction defines the goal and constraints.
- Verification evaluates accuracy and coherence.
- Synthesis turns results into decisions.
These layers bring structure to performance conversations and help managers evaluate judgment rather than output quantity. They also help identify when an employee is over-relying on automated work without examining the underlying assumptions.
This perspective aligns with patterns surfaced in Pinnacle conversations with leaders at HubSpot, Zapier, and Marriott: Managers need support as they learn to evaluate blended work. They want a shared language for discussing human contribution in workflows that include AI. HR can help establish that language through a refreshed performance rubric that emphasizes reasoning quality, decision impact, and reliability.
How does talent acquisition evolve as autonomy increases?
Recruiting priorities shift when AI handles more of the execution work. Jakub Pachocki, Chief Scientist at OpenAI, underlines that “autonomously delivering on larger research projects” gives a preview of the types of roles that will grow in importance. Organizations will seek people who can define ambiguous problems, reason across complex systems, and interpret technical outputs without losing context.
These hiring patterns already appear in teams experimenting with advanced automation. They favor adaptable thinkers who can synthesize information quickly and communicate clearly across functions. Technical depth remains valuable, cross-functional judgment becomes equally important.
Recruiters will need new assessments that surface these abilities with structured interviews that explore problem framing, assumption testing, and collaboration with automated tools become standard.
Another thread to pull on is the role of job design in reducing friction. Clear definitions of where humans add value help candidates understand the expectations of hybrid roles. CHROs can support hiring teams by developing job templates that highlight reasoning, ethics, and interpretation as core responsibilities in an AI-enabled environment.
A horizon that invites planning, experimentation, and clarity
The timeline outlined by OpenAI sets a direction. Organizations now have a multi-year window to prepare for changes in how teams work, collaborate, and develop. HR leaders are well positioned to guide this preparation. Their work includes updating job architecture, modernizing performance systems, and refining hiring practices to match the evolving landscape.
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