Here, I delve into an analytically speculative deep dive regarding one of those paths, specifically exploring the year-by-year elements of the most anticipated route, the linear path. Future posts will address each of the other remaining paths. The linear path involves AI progressing incrementally, one step at a time, until we reach AGI.

Let’s discuss this.

This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).

Advancing Toward AGI And ASI

First, some foundational information is necessary to set the stage for this significant discussion.

Extensive research is underway to further enhance AI. The primary objective is to either achieve artificial general intelligence (AGI) or potentially even the far-reaching possibility of attaining artificial superintelligence (ASI).

AGI refers to AI that is considered equivalent to human intelligence and can match our intellectual capabilities. ASI is AI that surpasses human intelligence and would excel in numerous, if not all, feasible areas. The concept is that ASI would outperform humans in every aspect. For more details on the nature of conventional AI compared to AGI and ASI, see my analysis at the link here.

We have not yet achieved AGI.

In fact, it is uncertain whether we will reach AGI, or that AGI might be attainable in decades or perhaps centuries from now. The AGI attainment dates circulating are highly varied and lack credible evidence or irrefutable logic. ASI is even more distant when considering our current state of conventional AI.

AI Experts' Consensus On AGI Date

Currently, efforts to predict when AGI will be attained mainly follow two paths.

First, there are prominent AI figures making bold individual predictions. Their confidence generates substantial media attention. These forecasts seem to be converging towards the year 2030 as the targeted date for AGI. A quieter approach is the periodic surveys or polls of AI experts. This collective wisdom approach is a form of scientific consensus. As discussed at the link here, the latest polls indicate that AI experts generally believe we will reach AGI by the year 2040.

Should you be influenced by the AI luminaries or more by the AI experts and their scientific consensus?

Historically, using scientific consensus as a method of understanding scientific positions has been relatively common and regarded as the standard approach. Relying on an individual scientist might result in their unique perspective. The advantage of consensus is that a majority or more of those in a given field collectively support the stated position.

The saying goes that two heads are better than one. In the case of scientific consensus, it might be dozens, hundreds, or thousands of heads that are better than one. For this discussion on the various pathways to AGI, I will proceed with the year 2040 as the consensus anticipated target date.

Besides the scientific consensus of AI experts, another newer and broader method of estimating when AGI will be achieved is known as AGI convergence-of-evidence or AGI consilience, which I discuss at the link here.

Seven Major Pathways

As mentioned, in a previous article I identified seven major pathways that AI will follow to become AGI (see the link here). The most commonly assumed path is the incremental progression route. The AI industry often refers to this as the linear path. It is essentially gradual and consistent. Each of the other remaining major routes involves various twists and turns.

Here’s my list of all seven major pathways leading us from contemporary AI to the cherished AGI:

• (1) Linear path (gradual progress): This AGI path captures the gradualist view, where AI advancement accumulates step by step through scaling, engineering, and iteration, ultimately reaching AGI.
• (2) S-curve path (plateau and resurgence): This AGI path reflects historical trends in the advancement of AI (e.g., early AI winters), allowing for leveling-up via breakthroughs after stagnation.
• (3) Hockey stick path (slow start, then rapid growth): This AGI path emphasizes the impact of a pivotal inflection point that reimagines and redirects AI advancements, possibly arising via theorized emergent capabilities of AI.
• (4) Rambling path (erratic fluctuations): This AGI path accounts for heightened uncertainty in advancing AI, including overhype-disillusionment cycles, and could also be punctuated by externally impactful disruptions (technical, political, social).
• (5) Moonshot path (sudden leap): Encompasses a radical and unexpected discontinuity in the advancement of AI, such as the famed envisioned intelligence explosion or similar grand convergence that spontaneously and nearly instantaneously arrives at AGI (for my in-depth discussion on the intelligence explosion, see the link here).
• (6) Never-ending path (perpetual muddling): This represents the harshly skeptical view that AGI may be unreachable by humankind, but we keep trying anyway, plugging away with an enduring hope and belief that AGI is around the next corner.
• (7) Dead-end path (AGI can’t seem to be attained): This indicates that there is a chance that humans might encounter a dead-end in the pursuit of AGI, which might be a temporary impasse or could be a permanent one such that AGI will never be attained no matter what we do.

You can apply those seven possible pathways to whatever AGI timeline you want to create.

Year-By-Year Futures Forecast

Let’s adopt a handy divide-and-conquer approach to identify what must presumably happen on a year-by-year basis to get from current AI to AGI.

Here’s how that works.

We are living in 2025 and are supposed to arrive at AGI by the year 2040. That’s essentially 15 years of elapsed time. In the specific case of the linear path, the key assumption is that AI is advancing in a stepwise manner each year. There aren’t any sudden breakthroughs or miraculous events that might occur. It is steady work and requires earnestly keeping our focus and getting the job done in those fifteen years ahead.

The idea is to map out the next fifteen years and speculate what will happen with AI in each respective year.

This can be done in a forward-looking mode and also a backward-looking mode. The forward-looking entails thinking about the progress of AI on a year-by-year basis, starting now and culminating in arriving at AGI in 2040. The backward-looking mode involves starting with 2040 as the deadline for AGI and then working back from that achievement on a year-by-year basis to arrive at the year 2025 (matching AI presently). This combination of forward and backward envisioning is a typical hallmark of futurecasting.

Is this kind of a forecast of the future definitive?

Nope.

If anyone could precisely outline the next fifteen years of what will happen in AI, they probably would be as prescient as Warren Buffett when it comes to predicting the stock market. Such a person could easily be awarded a Nobel Prize and ought to be one of the richest people ever.

All in all, this strawman that I present here is primarily intended to stimulate thought on how we can be future forecasting the state of AI. It is a conjecture. It is speculative. But at least it has a reasonable basis and is not entirely arbitrary or completely artificial.

I decided to use the fifteen years of reaching AGI in 2040 as an illustrative example. It could be that 2050 is the date for AGI instead, and thus this journey will play out over 25 years. The timeline and mapping would then have 25 years to deal with rather than fifteen. If 2030 is going to be the AGI arrival year, the pathway would need to be markedly compressed.

AGI Linear Path From 2025 To 2040

I chose to identify AI technological advancements for each of the years and added some brief thoughts on the societal implications as well. Here’s why. AI ethics and AI law are bound to become increasingly crucial and will to some degree promote AI advances and in other ways possibly hinder some AI advances, see my in-depth coverage of such tensions at the link here.

Here then is a strawman futures forecast year-by-year roadmap from 2025 to 2040 of a linear path getting us to AGI:

Year 2025: AI multi-modal models finally become robust and fully integrated into LLMs. Significant improvements in AI real-time reasoning, sensorimotor integration, and grounded language understanding occur. The use of AI in professional domains such as law, medicine, and the like increases. Regulatory frameworks remain sporadic and generally unadopted.

Year 2026: Agentic AI starts to flourish and become practical and widespread. AI systems with memory and planning capabilities achieve competence in open-ended tasks in simulation environments. Public interest in governing AI rises.

Year 2027: The use of AI large-scale world models boosts substantially improved AI capabilities. AI can now computationally improve from fewer examples via advancements in AI meta-learning. Some of these advances allow AI to be employed in white-collar jobs with a mild economic displacement, but only to a minor extent.

Year 2028: AI agents gain wide acceptance and are capable of executing multi-step tasks semi-autonomously in digital and physical domains, including robotics. AI becomes a key component in schools and education, co-teaching jointly with human teachers.

Year 2029: AI is advanced enough to have a generalized understanding of physical causality and real-world constraints through embodied learning. Concerns about AI as a job displacer reach heightened attention.

Year 2030: Self-improving AI systems begin modifying their own code under controlled conditions, improving efficiency without human input. This is a critical foundation. Some claim that AGI is now just a year or two away, but this is premature, and ten more years will first take place.

Year 2031: Hybrid AI consisting of integrated cognitive architectures unifying symbolic reasoning, neural networks, and probabilistic models has become the new accepted approach to AI. Infighting among AI developers as to whether hybrid AI was the way to go has now vanished. AI-based tutors fully surpass human teachers in personalization and subject mastery, putting human teachers at great job risk.

Year 2032: AI agents achieve human-level performance across most cognitive benchmarks, including abstraction, theory of mind (ToM), and cross-domain learning. This immensely exceeds prior versions of AI that did well on those metrics but not nearly to this degree. Industries begin to radically restructure and rethink their businesses with an AI-first mindset.

Year 2033: AI scalability alignment protocols improve in terms of human-AI values alignment. This opens the door to faster adoption of AI due to a belief that AI safety is getting stronger. Trust in AI grows. But so is societal dependence on AI.

Year 2034: AI interaction appears to be indistinguishable from human-to-human interaction, even as tested by those who are skilled in tricking AI into revealing itself. The role of non-human intelligence and how AI stretches our understanding of philosophy, religion, and human psychology has become a high priority.

Year 2035: AI systems exhibit genuine signs of self-reflection, not just routinized mimicry or parroting. Advances occur in having AI computationally learn from failure across domains and optimizing for long-term utility functions. Debates over some form of UBI (universal basic income) lead to various trials of the approach to aid human labor displacements due to AI.

Year 2036: AI advancement has led to fluid generalization across a wide swath of domains. Heated arguments take place about whether AGI is emerging, some say it is, and others insist that a scaling wall is about to be hit and that this is the best that AI will be. Nations begin to covet their AI and set up barriers to prevent other nations from stealing or copying the early AGI systems.

Year 2037: Advances in AI showcase human-like situational adaptability and innovation. New inventions and scientific discoveries are being led by AI. Questions arise about whether this pre-AGI has sufficient moral reasoning and human goal alignment.

Year 2038: AI systems now embody persistent identities, seemingly able to reflect on experiences across time. Experts believe we are on the cusp of AI reaching cognitive coherence akin to humans. Worldwide discourse on the legal personhood and rights of AI intensifies.

Year 2039: Some of the last barriers to acceptance of AI as nearing AGI are overcome when AI demonstrates creativity, emotional nuance, and abstract reasoning in diverse contexts. This was one of the last hurdles. Existential risks and utopian visions fully dominate public apprehensions.

Year 2040: General agreement occurs that AGI has now been attained, though it is still early days of AGI and some are not yet convinced that AGI is truly achieved. Society enters a transitional phase: post-scarcity economics, redefinition of human purpose, and consideration of co-evolution with AGI.

Reflecting On The Timeline

Ponder the strawman timeline and consider where you will be and what you will be doing during each of those fifteen years.

One perspective is that we are all along for the ride and there isn’t much that anyone can individually do. I disagree with that sentiment. Any of us can make a difference in how AI plays out and what the trajectory and impact of reaching AGI is going to be.

As per the famous words of Abraham Lincoln: “The most reliable way to predict the future is to create it.”

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