AI: The Gradual Yet Fast Takeoff

Picture skiing down a steep mountain. Each foot you descend may not seem dramatically different from the last, but before you know it, you’re hurtling down at breakneck speed. The pace of AI development is eerily similar. While improvements in AI capabilities may appear gradual when observed closely, the aggregate speed of progression can be staggering.

According to an analysis by Tom Davidson, once AI reaches the capacity to automate 20% of human jobs, it could leap to a full 100% in about three years. And human-level intelligence? That could be achieved just a year after the 100% automation mark. To the human observer, the technological advancements might seem incremental, a steady climb up the mountain. But in reality, we’re already accelerating down the steep slope, and there’s little time to catch our breath.

The role of computational power in this trajectory is significant. For example, modern AI systems like GPT-3 require around (10^{24}) calculations for training. To reach human-level intelligence, the computational needs could skyrocket to (10^{35}), an 11-order-of-magnitude increase. Once AI gains the ability to conduct its own research tasks, its rate of progress could become almost incomprehensible to human timelines.

There are debates, of course, surrounding the potential bottlenecks in AI progress. Will we experience a smooth exponential curve, or will there be discontinuous jumps that change the game overnight? However, the core argument remains: the seeming gradualness of AI advancements could very quickly scale to human-level intelligence and beyond. And as we stand on the precipice of this new era, the urgent question is not if, but when, and how soon we should be crafting contingency plans and safety measures for a world where AI could be the most powerful force.

The stakes are high, and the time to ponder these monumental shifts is running out. Let’s delve deeper into the nuanced realms of AI progress, computational needs, and the debates that surround the potential for an intelligence explosion.

The Role of Compute

Modern AI platforms, like GPT-3, require a staggering amount of computational power, around (10^{24}) calculations for training. To put that in perspective, it’s like performing a calculation for every star in the observable universe—several times over.

When we talk about AI reaching human-level intelligence, the computational needs take an astronomical jump to around (10^{35}) calculations. That’s an 11-order-of-magnitude increase, akin to comparing a single grain of sand to the entire landmass of Earth.

Interestingly, the computational requirements for an AI system capable of automating 20% of human jobs could be around (10^{28}) calculations. This sits neatly between our current AI capabilities and human-level intelligence. Once AI reaches this point, it’s not just a technological milestone but a signal flare, indicating that we’re closer than ever to full automation and human-level AI.

Arguably one of the most transformative moments in the AI timeline will be when these systems can engage in their own research tasks. The speed of progress will no longer be constrained by human limitations. Imagine running millions of AI research tasks in parallel, each making discoveries, learning, and evolving. The result would be an acceleration in AI progress that defies our usual understanding of timelines.

Computational power isn’t just a footnote in the narrative of AI; it’s the heartbeat that drives progress. While the numbers may seem abstract, they’re instrumental in understanding when more advanced forms of AI will emerge. Each increase in computational capability isn’t merely incremental; it’s a leap toward a future where AI systems are not only powerful but also transformative. It serves as a barometer for what’s coming, and based on these numbers, what’s coming could reshape our world in ways that are both exhilarating and unsettling.

Debates Around Intelligence Explosion

A central debate within the AI community revolves around the existence and impact of bottlenecks. Will AI development continue on a smooth, exponential curve, or will it encounter challenges that cause abrupt leaps or even setbacks? While some, like Tom Davidson, model AI progress as an exponential takeoff once AI can conduct its own research, others posit that unforeseen challenges could lead to discontinuous jumps in capabilities.

If bottlenecks are few and far between, the rise of super-smart AI systems could be meteoric. These AI assistants could transform entire economies in a matter of months, automating complex tasks and offering solutions to problems we’ve yet to solve. This isn’t just a technological revolution; it’s an economic and social upheaval the likes of which we’ve never seen.

The form that AI progress takes—whether a smooth curve or a series of leaps and bounds—has significant implications. An exponential curve provides some predictability, allowing us to prepare for what’s coming. Discontinuous jumps, on the other hand, could catch humanity off guard, necessitating rapid adjustments in strategy and possibly triggering a host of unintended consequences.

Models and forecasts are, at best, educated guesses. Davidson’s model has its proponents, as do theories that predict a more jagged trajectory. The diversity of opinion within the field underscores the complexity and unpredictability of AI’s future impact. It also serves as a clarion call for wide-ranging discussions on safety measures, ethical considerations, and contingency planning.

The debates around the potential for an intelligence explosion in AI are not mere academic exercises; they’re essential deliberations that could shape our collective future. Whether it’s the form that AI’s progress takes or the economic and social implications of super-smart AI systems, these debates force us to confront the vast unknowns in this rapidly evolving field. The one consensus is the urgency of the situation: Powerful AI systems capable of significant impact could arrive sooner than we anticipate. The time for academic debate is fast giving way to the time for actionable plans and precautionary measures. In grappling with these uncertainties, we’re not just forecasting technological trends; we’re charting the course of human destiny.

Key Takeaways

While the advancement of AI capabilities may appear incremental—a steady series of improvements—this gradual progression can be deceptive. Once AI hits certain milestones, such as the ability to automate 20% of human jobs, the velocity could spike dramatically. What looks like a gentle slope could very well be the edge of a cliff.

The computational demands for training AI are astronomical and will only continue to grow as we inch closer to achieving human-level intelligence. But the importance of compute extends beyond sheer numbers. It serves as a barometer for the scale and speed of advancements, signaling key transition points that could bring us into uncharted territory.

Given the velocity at which AI could scale to human-level intelligence and beyond, contingency planning isn’t just prudent—it’s non-negotiable. Whether it’s the exponential model predicted by Davidson or a more fragmented progression, the end result could be the same: AI systems with capabilities that dwarf our own, emerging in a timeline that leaves little room for procrastination.

The discourse around whether AI development will be exponential or marked by discontinuous jumps isn’t trivial. It has profound implications for how we prepare for this future. An exponential curve offers some level of predictability, whereas discontinuous jumps could lead to reactive, potentially chaotic, responses.

As we stand on the cusp of a new era defined by AI, the stakes couldn’t be higher. While the speed of progress may be up for debate, the transformative power of advanced AI systems is not. We’re looking at a future where AI could either augment human capabilities or replace them altogether, and the timeline for this seismic shift is growing ever shorter. Whether it’s the computational milestones that serve as harbingers of change, or the debates that seek to map out the trajectory, each element serves as a piece in a larger puzzle that we’re racing to complete. And as we slot each piece into place, we’re not just constructing a picture of AI’s future; we’re shaping our own. The clock is ticking, and the time for action, for preparation, for ethical and practical considerations, is now.

The Immediacy of the Infinite

As we grapple with the paradigms of artificial intelligence, we find ourselves not just at the frontier of technological discovery, but at the edge of human comprehension. The sheer velocity of AI advancements challenges our ability to forecast, to adapt, and perhaps most critically, to prepare for a world irrevocably altered by machine intelligence. The dialogue we engage in today—be it about the rate of AI progression, the computational underpinnings, or the shape of the AI development curve—has ramifications that reverberate far beyond academic discourse and industry chatter. These discussions shape policies, inform ethical considerations, and lay the groundwork for a society either uplifted or undone by AI.

The grand irony here is that while we birth these algorithms, train them, and set them upon tasks that range from the mundane to the monumental, their potential outstripping of human capabilities forces us to confront our own limitations. Whether AI development takes the form of a smooth, exponential curve or a series of discontinuous leaps, the endgame remains the same: a reality where machine intelligence, for better or worse, becomes a defining force in shaping human destiny.

What’s indisputable is the urgency that permeates these considerations. The exponential models, the computational milestones, the debates over intelligence explosions—they all point to the immediacy of the questions we face. And these questions, pregnant with both promise and peril, demand answers not in some distant future but in the immediate present. We’re tasked with contemplating not just the “what” and the “how,” but the profound “what-ifs” that stretch our intellectual and ethical boundaries.

So as we hurtle down this mountain of rapid AI development, let us not forget that the tracks we lay in the snow are not just for us but for generations to come. It’s an endeavor that requires not just technological prowess but a deep, soul-searching inquiry into what it means to be human in an age where the line between man and machine is becoming ever more blurred. And as we navigate this existential terrain, let’s do so with the understanding that the choices we make today will echo through the corridors of tomorrow, reverberating in ways we can only begin to imagine.

The clock isn’t just ticking; it’s accelerating. And in this high-stakes race against time, our greatest asset may well be the recognition that the future of AI isn’t some abstract concept lying beyond the horizon. It’s here, it’s now, and it demands our immediate and undivided attention.


The Gradual Yet Fast Takeoff Argument

  • Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.

The Role of Compute

  • Amodei, D. et al. (2018). AI and Compute. OpenAI Blog.
  • Cotra, A. (2022). An Update to the Biological Anchors Model.

Debates Around Intelligence Explosion

  • Yudkowsky, E. (2013). Intelligence Explosion Microeconomics. Machine Intelligence Research Institute.
  • Christiano, P. (2018). Takeoff Speeds. Medium.
  • Davidson, T. (2023). What a Compute-Centric Framework Says About Takeoff Speeds.

Key Takeaways

  • Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Penguin.
  • Ord, T. (2020). The Precipice: Existential Risk and the Future of Humanity. Hachette Books.


  • Tom Serrano

    Thomas "Tom" Serrano, is a proud Cuban-American dad from Miami, Florida. He's renowned for his expertise in technology and its intersection with business. Having graduated with a Bachelor's degree in Computer Science from the East Florida, Tom has an ingrained understanding of the digital landscape and business.Initially starting his career as a software engineer, Tom soon discovered his affinity for the nexus between technology and business. This led him to transition into a Product Manager role at a major Silicon Valley tech firm, where he led projects focused on leveraging technology to optimize business operations.After more than a decade in the tech industry, Tom pivoted towards writing to share his knowledge on a broader scale, specifically writing about technology's impact on business and finance. Being a first-generation immigrant, Tom is familiar with the unique financial challenges encountered by immigrant families, which, in conjunction with his technical expertise, allows him to produce content that is both technically rigorous and culturally attuned.

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