Big Bets On Which Of These Pathways Will Push Today's AI To Become Prized AGI

Let's explore the potential paths to Artificial General Intelligence (AGI). This analysis is part of my ongoing Forbes column on AI advancements, delving into the complexities of achieving AGI and Artificial Superintelligence (ASI). (See related articles here.)

The Pursuit of AGI and ASI

Before we delve into the specifics, let's establish some foundational concepts. Current AI research aims to achieve either AGI—artificial intelligence comparable to human intellect—or ASI—artificial intelligence surpassing human intelligence in all aspects. ASI represents a hypothetical future where AI significantly outperforms human capabilities. For a detailed comparison of conventional AI, AGI, and ASI, please refer to my previous analysis (link here).

Currently, AGI remains elusive. The timeline for its achievement is highly uncertain, with predictions ranging from decades to centuries, lacking substantial evidence or logical basis. The prospect of ASI is even more distant.

Projected AGI Timelines: A Critical Look

Given AGI's perceived greater likelihood of near-term achievement compared to ASI, let's examine potential timelines. Surveys of AI experts suggest 2040 as a possible date for AGI realization. However, some prominent figures predict much sooner—within 3-5 years—pointing to 2028-2030. I find this highly improbable, suspecting that these optimistic projections involve a narrower definition of AGI to support their claims. (See my analysis of various AGI predictions and definitions here).

For this discussion, let's use 2040 as a hypothetical target, giving us a 15-year timeframe. Let's analyze how this period might unfold.

Timeline Scenarios: Incremental Progress vs. Breakthroughs

Currently, we are in mid-2025. Reaching AGI by 2040 presents a significant challenge. One possibility is a steady, incremental progression, with roughly equal advancements each year. A 7% annual improvement, sustained over 15 years, could theoretically lead to AGI by 2040 (using rounded figures for simplification).

However, some argue that this incremental approach is insufficient. They believe current methodologies lack scalability and suffer from a homogenized approach, relying on similar algorithms and techniques. They contend that radical innovation is necessary to achieve AGI. (See my coverage of this debate here).

The "Miracle" Scenario: A Sudden Breakthrough

Critics of the incremental approach posit a scenario where a groundbreaking, unforeseen advancement by a single developer dramatically accelerates AGI development. This "moonshot" approach suggests that after years of modest progress, a sudden innovation could rapidly lead to AGI. This could occur anywhere within the 15-year timeframe, perhaps around year 13 or 14, or even later.

The challenge with this scenario is its reliance on a highly improbable breakthrough—a "miracle"—to bridge the gap. This is akin to relying on an unforeseen event to solve a complex problem.

Seven Potential Pathways to AGI

I've identified seven potential pathways to AGI:

1. Linear Path (gradual progress): Steady advancement through scaling, engineering, and iteration.
2. S-Curve Path (plateaus and resurgence): Reflects historical trends in AI development, including periods of stagnation followed by breakthroughs.
3. Hockey Stick Path (slow start, rapid growth): A significant inflection point dramatically accelerates progress, potentially due to emergent AI capabilities.
4. Rambling Path (erratic fluctuations): Accounts for uncertainty, hype cycles, and external disruptions.
5. Moonshot Path (sudden leap): A radical, unexpected breakthrough, possibly an "intelligence explosion." (For details on the intelligence explosion, see here).
6. Never-Ending Path (perpetual struggle): AGI remains perpetually out of reach, despite continuous efforts.
7. Dead-End Path (insurmountable obstacles): AGI proves unattainable, regardless of efforts.

These pathways can be applied to various timelines. My 15-year example (to 2040) is illustrative; the timeline could extend to 2050 or even be compressed to 2028.

Assessing the Probabilities

Belief in a specific pathway influences betting strategies. A linear path suggests continued focus on current methods. A moonshot path necessitates exploring unconventional ideas. Similar strategies apply to each pathway.

Based on discussions with colleagues, the S-curve appears most likely, aligning with technological development trends and acknowledging the limitations of current approaches. The moonshot approach is considered least likely, reflecting skepticism about the probability of a sudden, miraculous breakthrough.

However, this shouldn't discourage innovation. As the saying goes, "Miracles happen to those who believe in them," and the same might apply to the pursuit of AGI. Which pathway do you find most compelling?

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