Let’s dive in.

This exploration of a groundbreaking AI development is part of my ongoing coverage for Forbes on the latest advancements in artificial intelligence, including breaking down and clarifying various complex AI-related topics (see the link here). For readers who have followed my series on AGI development paths, please note that I include similar introductory context at the beginning of this article as I did before, to bring new readers up to speed.

On the Road to AGI and ASI

To begin, some basic concepts are necessary to frame this critical discussion.

There is a vast amount of research currently underway aimed at pushing AI forward. The ultimate objective for many is to reach artificial general intelligence (AGI), or even more ambitiously, artificial superintelligence (ASI).

AGI is defined as AI that equals human cognitive ability and can effectively mirror our intellectual capacities. ASI, on the other hand, represents AI that surpasses human intelligence, potentially in every conceivable way. In theory, ASI would be capable of outperforming humans in every intellectual endeavor. For a deeper understanding of how standard AI compares to AGI and ASI, check my detailed analysis here.

We have not yet achieved AGI.

In fact, it remains uncertain whether AGI is even possible, or if it will take decades or even centuries to accomplish. Predictions about when AGI might be achieved vary wildly, with little in the way of solid evidence or rigorous logic to back them up. ASI remains even further from our current capabilities in AI development.

What Do AI Experts Say About AGI Timelines?

Currently, there are mainly two approaches to predicting when AGI might be realized.

The first involves prominent AI figures making bold, individual predictions that often dominate media headlines. These forecasts have recently converged around the year 2030 as a likely milestone for AGI. The second approach involves surveys or polls of AI experts conducted periodically, which represent a more collective, consensus-based view. As outlined in the link here, the most recent expert surveys indicate a general belief that AGI will be achieved by 2040.

Which should you trust more — the high-profile predictions or the broader scientific consensus?

Historically, scientific consensus has been widely accepted as a reliable method for gauging the direction of scientific progress. Relying on a single expert can lead to skewed or idiosyncratic views. The advantage of a consensus is that it reflects the collective opinion of a large group of professionals in the field.

The saying goes, “two heads are better than one,” but in the case of scientific consensus, we might be talking about hundreds or even thousands of minds working toward a shared understanding. For the purposes of this article and the discussion of potential AGI pathways, I’ll use 2040 as the target year based on the current expert consensus.

In addition to expert opinion, a newer, more comprehensive method for estimating AGI timelines is known as “AGI convergence-of-evidence” or “AGI consilience,” which I elaborate on here.

Seven Key Development Pathways

As previously discussed in an earlier article, I identified seven primary pathways through which AI might evolve into AGI (see the link here). Here is my list of these seven AGI development routes, which take us from today’s AI systems to the ultimate goal of AGI:

• (1) Linear path (gradual progress): This pathway represents a steady, incremental advancement of AI via continuous engineering and scaling, eventually leading to AGI.
• (2) S-curve path (stagnation followed by growth): This reflects historical AI development trends, including periods of stagnation (like AI winters), followed by breakthroughs that reignite progress.
• (3) Hockey stick path (slow beginning, then rapid acceleration): This model suggests a key turning point that catalyzes rapid AI development, possibly due to emergent capabilities.
• (4) Rambling path (unpredictable changes): Accounts for uncertainty in AI development, including hype cycles and external disruptions (technological, political, or societal).
• (5) Moonshot path (sudden breakthrough): Envisions a radical and unexpected leap in AI development, such as an intelligence explosion or other sudden convergence leading to AGI (see my in-depth analysis on this here).
• (6) Never-ending path (continuous effort): Reflects a pessimistic view that AGI may be unattainable, but humanity continues to pursue it with hope.
• (7) Dead-end path (AGI remains unreachable): Suggests that despite efforts, we may reach a permanent or long-term impasse in achieving AGI.

These seven paths can be applied to any AGI timeline you wish to consider.

Forecasting the Future of AI

Let’s break this down systematically to understand what must happen to transition from current AI systems to AGI.

We are in the year 2025, and the goal is to reach AGI by 2040 — a span of 15 years. The aim is to outline the next fifteen years and hypothesize about AI’s evolution.

This can be approached in two ways: forward-looking and backward-looking. The forward-looking method involves projecting AI progress year by year from now until AGI is achieved in 2040. The backward-looking approach starts from the assumption of AGI being achieved in 2040 and works backward to match today’s AI capabilities in 2025. Combining both approaches is a common technique in future forecasting.

Is such a prediction foolproof?

Absolutely not.

If someone could accurately map out the next fifteen years of AI development, they would be as prescient as Warren Buffett in stock market predictions. Such an individual would likely be a Nobel laureate and among the wealthiest people in history.

This proposed roadmap is primarily intended to stimulate thinking about how we can envision the future of AI. It's speculative and theoretical. However, it is grounded in reasonable assumptions rather than being entirely arbitrary.

For this exercise, I used the 2040 AGI timeline as an example. It could just as easily be 2050, which would stretch the timeline to 25 years. Alternatively, if AGI arrives in 2030, the development path would need to be significantly compressed.

AGI S-Curve Development Path From 2025 To 2040

The S-curve model is unique in that it follows an S-shaped progression — initial progress, followed by a plateau, and then a resurgence of advancement. This contrasts sharply with a linear model, where AI development proceeds at a steady pace each year. I previously discussed the linear path in detail here.

For simplicity, let's assume that over the 15-year period, each phase of the S-curve lasts five years: starting phase (2025–2030), plateau (2030–2035), and resurgence (2035–2040). While the actual durations could vary (e.g., 3 years of growth, 8 years of stagnation, and 4 years of rapid progress), using five-year segments works well for this discussion.

Here's how the S-curve phases will be outlined:

• Years 2025–2030: Initial phase of the S-curve (growth)
• Years 2030–2035: Middle phase of the S-curve (plateau)
• Years 2035–2040: Final phase of the S-curve (resurgence)

Note that the years 2030 and 2035 may overlap between phases. For clarity, I’ll group the years into these three phases.

Here’s a speculative roadmap for AI development from 2025 to 2040 under the S-curve model:

Years 2025–2030 (Initial Growth Phase):

• Multi-modal AI models become fully integrated with LLMs, significantly improving real-time reasoning, sensorimotor coordination, and contextual language understanding.
• Agentic AI begins to mature, with systems gaining memory and planning capabilities, enabling them to handle complex tasks in simulated environments.
• Large-scale world models enhance AI learning efficiency, allowing systems to learn from fewer examples through meta-learning advancements.
• AI agents gain broader acceptance and are capable of executing multi-step tasks semi-independently across digital and physical domains, including robotics applications.
• AI systems develop a generalized understanding of physical laws and real-world constraints through embodied learning techniques.

Years 2030–2035 (Plateau Phase):

• Expansion of AI agent capabilities continues, but overall functionality plateaus.
• AI integration into robotics becomes more sophisticated, enabling widespread use of mobile, affordable robots in both homes and businesses.
• Concerns arise that AI progress has stalled, with no significant breakthroughs emerging. AI is being widely deployed, but the technology itself isn’t advancing much further.
• Debates intensify over whether AGI is truly achievable or if AI has hit an insurmountable barrier. Anxiety grows within the AI community, with fears of another AI winter or prolonged stagnation.

Years 2035–2040 (Resurgence Phase):

• AI systems begin self-mod

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