Sam Altman's goal of an "automated AI research intern" by 2026 and a full "researcher" by 2028 is not about simple task automation. It is a direct push toward creating recursively self-improving systems—AI that can discover new methods to improve AI models, aiming for an "intelligence explosion."
Related Insights
OpenAI co-founder Ilya Sutskever suggests the path to AGI is not creating a pre-trained, all-knowing model, but an AI that can learn any task as effectively as a human. This reframes the challenge from knowledge transfer to creating a universal learning algorithm, impacting how such systems would be deployed.
Coined in 1965, the "intelligence explosion" describes a runaway feedback loop. An AI capable of conducting AI research could use its intelligence to improve itself. This newly enhanced intelligence would make it even better at AI research, leading to exponential, uncontrollable growth in capability. This "fast takeoff" could leave humanity far behind in a very short period.
Julian Schrittwieser, a key researcher from Anthropic and formerly Google DeepMind, forecasts that extrapolating current AI progress suggests models will achieve full-day autonomy and match human experts across many industries by mid-2026. This timeline is much shorter than many anticipate.
Silicon Valley insiders, including former Google CEO Eric Schmidt, believe AI capable of improving itself without human instruction is just 2-4 years away. This shift in focus from the abstract concept of superintelligence to a specific research goal signals an imminent acceleration in AI capabilities and associated risks.
OpenAI is launching initiatives to certify millions of workers for an AI-driven economy. However, their core mission is to build artificial general intelligence (AGI) designed to outperform humans, creating a paradox where they are both the cause of and a proposed solution to job displacement.
OpenAI announced goals for an AI research intern by 2026 and a fully autonomous researcher by 2028. This isn't just a scientific pursuit; it's a core business strategy to exponentially accelerate AI discovery by automating innovation itself, which they plan to sell as a high-priced agent.
Companies like OpenAI and Anthropic are not just building better models; their strategic goal is an "automated AI researcher." The ability for an AI to accelerate its own development is viewed as the key to getting so far ahead that no competitor can catch up.
A key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.
The ultimate goal for leading labs isn't just creating AGI, but automating the process of AI research itself. By replacing human researchers with millions of "AI researchers," they aim to trigger a "fast takeoff" or recursive self-improvement. This makes automating high-level programming a key strategic milestone.
Ilya Sutskever argues that the AI industry's "age of scaling" (2020-2025) is insufficient for achieving superintelligence. He posits that the next leap requires a return to the "age of research" to discover new paradigms, as simply making existing models 100x larger won't be enough for a breakthrough.
