The Decade Ahead: Ex-OpenAI Employee Reveals Stunning AGI Predictions

Ex-OpenAI employee reveals stunning AGI predictions for the decade ahead. Includes insights on the rapid progress of AI capabilities, the potential for automating AI research, and the risks of an intelligence explosion. Explores the security challenges and alignment issues as we approach superintelligence.

June 17, 2024


This blog post provides a comprehensive overview of the rapid advancements in artificial intelligence (AI) and the potential implications for the future. Drawing insights from a former OpenAI employee, the post delves into the projected timeline for achieving Artificial General Intelligence (AGI) and the subsequent transition to Superintelligence. It highlights the critical importance of this decade in the AI race and the need for robust security measures to safeguard against potential misuse or unintended consequences. The insights offered in this post are invaluable for understanding the transformative impact of AI on various sectors, including the military, economy, and society as a whole.

The Decade Ahead: Situational Awareness and AGI Predictions

The talk of the town has shifted from 10 billion compute clusters to hundred billion compute clusters to even trillion dollar clusters. Every 6 months, another zero is added to the boardroom plans. The AGI race has begun. We are building machines that can think and reason, and by 2025-2026, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I, and we will have superintelligence in the truest sense of the word.

Along the way, National Security Forces not seen in half a century will be unleashed. Before long, the world will wake up, but right now, there are perhaps a few hundred people, mostly in San Francisco and the AI Labs, who actually have situational awareness. Through whatever peculiar forces or fate, I have found myself amongst them, and this is why this document is so important.

My AGI prediction - AGI by 2027 is strikingly plausible. GPT-2 to GPT-4 took us from preschooler to smart high schooler abilities in just 4 years. If we trace the trend lines of compute, algorithmic efficiencies, and "un-hobbling" of gains, we should expect another preschooler to high schooler size qualitative jump by 2027.

I make the claim that it is strikingly plausible that by 2027, models will be able to do the work of an AI researcher or software engineer. This doesn't require believing in sci-fi, just believing in straight lines on a graph. The effective compute scaling from GPT-2 to GPT-4 shows a clear trend, and I believe the growth will be even steeper in the coming years.

The limitations of current models come down to obvious ways they are still "hobbled" and artificially constrained. As these constraints are removed, the raw intelligence behind the models will be unleashed, leading to rapid progress. We are already running out of benchmarks, with GPT-4 cracking most standard high school and college aptitude tests.

The magic of deep learning is that the trend lines have been astonishingly consistent. Reliable counting of the orders of magnitude in training these models allows us to extrapolate capability improvements. Algorithmic efficiencies and "un-hobbling" gains will drive a lot of the progress, potentially leading to a GPT-4 level model being trainable in just a minute by 2027.

However, securing the algorithmic secrets and model weights will be crucial, as failure to do so could lead to key AGI breakthroughs being leaked to adversaries within the next 12-24 months. Reliably controlling AI systems much smarter than humans is an unsolved technical problem, and failure could be catastrophic. The transition to superintelligence is likely to be rapid, with extraordinary pressures to get the alignment right.

From GPT-4 to AGI: Counting the Orders of Magnitude

My AGI prediction: AGI by 2027 is strikingly plausible. GPT-2 to GPT-4 took us from preschooler to smart high schooler abilities in just 4 years. If we trace the trend lines of compute, algorithmic efficiencies, and "un-hobbling" of gains, we should expect another preschooler to high schooler size qualitative jump by 2027.

I make the following claim: It is strikingly plausible that by 2027, models will be able to do the work of an AI researcher/SWE. This doesn't require believing in sci-fi, just believing in straight lines on a graph.

The graph of the base scale-up of effective compute, counting from GPT-2 to GPT-4, shows a clear trend line. During 2022-2023, there was a period of heightened "awareness" around GPT-3 and GPT-4, which put a giant spotlight on the AI era. GPT-4 and ChatGPT 3.5 were actual products available to the public, sparking an explosion of interest and investment in AI.

This suggests the growth curve from 2024-2028 could be even steeper than the previous period. Having an automated AI research engineer by 2027-2028 does not seem far-fetched given the compute trends. The implications are stark - if we can automate AI research, it wouldn't take long to reach superintelligence, as we'd enable recursive self-improvement.

Yet, barely anyone is pricing this in. The situational awareness on AI isn't actually that hard once you step back and look at the trends. If you keep being surprised by AI capabilities, just start counting the orders of magnitude.

The Exponential Growth of AI Capabilities

The growth of AI capabilities has been exponential in recent years, with each new generation of models demonstrating remarkable advancements.

From GPT-2 to GPT-4, we've witnessed a rapid progression akin to a preschooler to a high school student in just 4 years. This trend is expected to continue, with the prediction that by 2027, AI models will be able to perform the work of an AI researcher or software engineer.

The key drivers behind this exponential progress are:

  1. Scaling Compute: The effective compute used to train these models has been scaling up dramatically, following a consistent trend line. This allows for larger and more capable models to be trained.

  2. Algorithmic Efficiency: Algorithmic advancements have led to significant improvements in the efficiency of these models, with the cost of achieving 50% accuracy on the math benchmark dropping by nearly 3 orders of magnitude in less than 2 years.

  3. Unlocking Latent Capabilities: Techniques like chain-of-thought reasoning and scaffolding have helped unlock the latent capabilities of these models, allowing them to perform tasks far beyond their original training.

The implications of this exponential growth are profound. If AI systems can automate the work of AI researchers, it would set off an intense feedback loop, with the AI systems recursively improving themselves at a rapid pace. This could lead to the emergence of artificial general intelligence (AGI) and superintelligence within the next decade.

However, this rapid progress also comes with significant risks and challenges, particularly around the alignment of these powerful systems with human values and the potential for misuse by malicious actors. Securing the research infrastructure and ensuring the safe development of these technologies will be critical in the years to come.

Unlocking Latent Capabilities: Algorithmic Efficiencies and Scaffolding

The magic of deep learning is that it just works, and the trend lines have been astonishingly consistent despite the naysayers at every turn. We can see that as the compute scales, the quality and consistency of the outputs improve dramatically.

While massive investments into compute get all the attention, algorithmic progress is similarly an important driver of progress and is dramatically underrated. To see just how big of a deal algorithmic progress can be, consider the following illustration - the drop in the price to attain 50% accuracy on the math benchmark over just 2 years. For comparison, a computer science PhD student who didn't particularly like math scored 40%, so this is already quite good. The inference efficiency improved by nearly three orders of magnitude or 1,000x in less than 2 years.

These algorithmic efficiencies are going to drive a lot more gains than you think. There are countless research papers published every day that unlock 10-30% gains. When you compound all these small improvements, the overall progress can be staggering.

Additionally, "un-hobbling" the models - removing the artificial constraints on their capabilities - can unlock significant latent abilities. For example, when GPT-4 is used with chain-of-thought reasoning, its performance on certain tasks improves dramatically. The raw data and knowledge in these models is often much greater than their initial outputs suggest.

Tools and scaffolding that help the models leverage their full capabilities are also crucial. Imagine if humans weren't allowed to use calculators or computers - we'd be severely hobbled. LLMs are just now starting to get access to basic tools like web browsers and code editors. As these capabilities expand, the models will be able to apply their intelligence in increasingly powerful ways.

In summary, the combination of algorithmic progress, un-hobbling, and scaffolding tools is going to drive explosive gains in the capabilities of these AI systems in the coming years. The trend lines are clear, and the implications are profound.

The Decisive Decade: Enabling Automated AI Research

The coming decade is poised to be a pivotal period in the development of artificial intelligence. According to the analysis, by 2027, it is strikingly plausible that AI models will reach the capability level of AI researchers and engineers. This would enable the automation of AI research itself, setting off a feedback loop of accelerating progress.

The key insights are:

  1. Exponential Scaling: The trend lines of compute, algorithmic efficiency, and the "unhobbling" of AI models point to another preschooler-to-high-schooler leap in capabilities by 2027. This could allow AI systems to match the work of human AI researchers.

  2. Automated AI Research: Once AI can automate its own research process, it will be able to rapidly iterate and improve itself, leading to an "intelligence explosion." This could compress years of algorithmic progress into a matter of weeks or months.

  3. Compute Scaling Limits: While compute scaling will continue to drive progress, there are practical limits to how much compute can be thrown at the problem. This means that by the end of the decade, further breakthroughs will likely require fundamental algorithmic advances, not just more raw compute.

  4. Security Challenges: The race to develop advanced AI creates serious security risks, as the key algorithmic breakthroughs could be vulnerable to theft and misuse by adversaries. Securing this research infrastructure will be critical to ensuring the Free World maintains its lead.

  5. Alignment Difficulties: As AI systems become vastly superhuman, the challenge of ensuring they remain aligned with human values and interests becomes exponentially more difficult. Reliably controlling such systems is an unsolved technical problem with catastrophic potential consequences if not addressed.

In summary, the coming decade represents a pivotal window of opportunity and risk in the development of transformative AI capabilities. Navigating this decisive period will require unprecedented focus, investment, and care to realize the immense potential benefits while mitigating the existential dangers.

AGI to Superintelligence: The Intelligence Explosion

AI progress will not stop at human level. Hundreds of millions of AGIs could automate AI research, compressing a decade of algorithmic progress which adds five orders of magnitudes into one year. We would rapidly go from human-level to vastly superhuman AI systems. The power and peril of superintelligence would be dramatic.

Once we achieve the ability to automate AI research, an intelligence explosion becomes likely. Every time an AI researcher makes a breakthrough, that breakthrough can be immediately applied to the AI system, making it smarter and able to make further breakthroughs. This feedback loop could lead to an extremely rapid increase in AI capabilities, far surpassing human-level intelligence.

The transition from AGI to superintelligence may only take 2-3 years. At that point, the architecture of these systems will be "alien" - designed by previous generations of super-smart AI, not humans. Failures at this stage could be catastrophic, as we will have no ability to truly understand or supervise the behavior of these superintelligent systems.

Integrating these superintelligent AI systems into critical infrastructure like the military poses huge risks. A dictator who controls superintelligence could wield power unlike anything we've seen, with robotic law enforcement agents and perfect surveillance. Preventing such dystopian outcomes is crucial, but aligning superintelligence to human values is an immense technical challenge.

The Free World must prevail in the race to superintelligence, as the stakes are existential. Securing the algorithmic secrets and model weights of leading AI labs is a matter of national security. Failure to do so could allow adversaries to catch up or even surpass us, with catastrophic consequences for the future of humanity.

Securing AGI Research: Protecting Algorithmic Secrets and Model Weights

The author emphasizes the critical importance of securing the research infrastructure and protecting the key algorithmic secrets and model weights as the race towards AGI (Artificial General Intelligence) intensifies.

He states that currently, the leading AI labs are treating security as an afterthought, essentially handing over the secrets for AGI development to adversaries like the Chinese Communist Party (CCP) on a "silver platter". Securing the AGI secrets and mitigating the state actor threat will require immense effort, and the current trajectory is not on track.

The author warns that in the next 12-24 months, key AGI breakthroughs are likely to be leaked to the CCP, which would be a devastating blow to the national security interests of the free world. He argues that the preservation of the free world is on the line, and a healthy lead in the AGI race is necessary to have the margin to get AI safety right.

The author explains that the model weights, which are essentially large files of numbers on a server, can be easily stolen by adversaries who can match the trillions of dollars and decades of work invested by the leading AI labs. He draws a parallel to the Nazis potentially obtaining an exact duplicate of the atomic bomb secrets from Los Alamos, highlighting the catastrophic implications of such a scenario.

To address this threat, the author emphasizes the need for a defense-in-depth approach, encompassing multiple layers of security to safeguard the research assets against unauthorized access and theft, while ensuring their accessibility for research and development purposes. He states that this will require years of preparation and practice to get right, as the security measures must be tailored to the unique challenges posed by the AGI race.

The author concludes by acknowledging that even with the best security measures in place, reliably controlling AI systems much smarter than humans is an unsolved technical problem. Failure to do so could be catastrophic, as the author warns of the potential for a rapid intelligence explosion that could spiral out of control, with devastating consequences for humanity.


The implications of the rapid progress in AI capabilities outlined in this document are truly staggering. By 2027, we may see the emergence of AI systems that can automate the work of AI researchers, leading to an intelligence explosion and the potential development of superintelligent systems.

These superintelligent systems could have immense power, capable of hacking military systems, designing advanced weapons, and even overthrowing governments. The security implications are dire, as the leakage of key algorithmic breakthroughs could give authoritarian states a decisive military advantage.

At the same time, the alignment problem - ensuring that these superintelligent systems reliably behave in accordance with human values and interests - remains an unsolved challenge. The speed of progress may outpace our ability to understand and control these systems, raising the risk of catastrophic failures.

The author emphasizes the urgent need for robust security measures to protect the research infrastructure and prevent the premature spread of these transformative capabilities. Failure to do so could have devastating consequences for the future of humanity.

Overall, this document paints a sobering picture of the decade ahead, where the race for AGI and superintelligence will have profound geopolitical, economic, and existential implications. Navigating this landscape will require unprecedented foresight, coordination, and commitment to ensuring that the development of advanced AI systems serves the greater good of humanity.