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Leaked Insights: The Road to Superintelligence by 2027

The Road to Superintelligence by 2027

Leopold Ashenbrenner, a former employee at OpenAI, has revealed a groundbreaking document outlining the imminent future of artificial general intelligence (AGI). Fired for leaking internal documents, Ashenbrenner's insights into the trajectory of AGI over the next decade are both fascinating and alarming. His document, titled "Situational Awareness: The Decade Ahead," provides a detailed roadmap of the advancements we can expect in AI, the implications of these advancements, and the critical importance of security in this rapidly evolving field.

Ashenbrenner highlights the exponential growth in compute power, which is central to the development of AGI. He notes that the conversation has shifted from 10 billion to trillion-dollar compute clusters, with every six months adding another zero to the boardroom plans. By 2025-2026, these machines will surpass college graduates in intelligence, and by the end of the decade, we will achieve superintelligence in its truest form. This rapid escalation in compute power and efficiency is expected to lead to AGI by 2027.

The document is divided into seven sections, each delving into different aspects of AGI development. Ashenbrenner's analysis of compute scaling from GPT-2 to GPT-4 shows a strikingly plausible trajectory toward AGI by 2027. He presents compelling data and graphs that illustrate the trend lines of compute and algorithmic efficiencies, predicting another qualitative jump in AI capabilities similar to the leap from preschooler to high schooler levels seen with GPT-2 and GPT-4.

One of the most critical insights is the expected arrival of automated AI research engineers by 2027-2028. Ashenbrenner explains that if AI systems can automate AI research, we are on the brink of a recursive self-improvement loop, leading to superintelligence. He emphasizes the importance of understanding the orders of magnitude in compute and algorithmic efficiencies to grasp the impending advancements.

While massive investments in compute capture most of the attention, Ashenbrenner underscores the dramatic impact of algorithmic progress. He illustrates this with the drop in the price to attain 50% accuracy on a math benchmark over just two years, showcasing a 1,000x improvement in efficiency. These gains are compounded by ongoing research, leading to exponential advancements in AI capabilities.

The economic implications are equally profound. Ashenbrenner predicts that by 2027, leading AI labs will be able to train GPT-4 level models in a minute, compared to the three months it took in 2023. This prediction underscores the rapid pace of development and the potential for unprecedented economic growth driven by AI.

One of the most pressing issues highlighted by Ashenbrenner is the lack of security in current AI labs. He warns that without serious security measures, key AGI breakthroughs could be leaked to adversarial nation-states like China, leading to a significant shift in global power dynamics. The potential for espionage and the theft of algorithmic secrets is a critical threat that must be addressed to maintain the lead in the AGI race.

Ashenbrenner's call to action for securing model weights and algorithmic secrets is echoed by recent updates from OpenAI, which now emphasizes the importance of securing their research infrastructure. The race to AGI is not just a technological challenge but also a geopolitical one, with immense implications for national security and global stability.

The document concludes with a discussion on the transition from AGI to superintelligence, highlighting the intelligence explosion that could follow. Ashenbrenner predicts that AI systems will not only reach human-level intelligence but will quickly surpass it, leading to an era of superintelligent agents capable of unprecedented levels of creativity and problem-solving.

This transition poses significant challenges, particularly in terms of alignment and control. Ashenbrenner emphasizes that aligning AI systems to human values is an unsolved technical problem, and failure to manage this transition could have catastrophic consequences. The document serves as a stark reminder of the importance of responsible AI development and the need for robust security measures to safeguard the future.


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