The Five Stages of AI Development and What Comes Next

The Five Stages of AI Development and What Comes Next

By Janpha Thadphoothon

Let's talk about intelligence. I am sure you would agree with me that the development of artificial intelligence, or what we simply call AI, is one of the most transformative and intriguing technological advances humanity has ever witnessed. As we know, AI has gradually but persistently found its way into nearly every aspect of our daily lives—from the phones we use, the cars we drive, to the way we shop, study, and even fall in love. The news has it that AI is no longer the stuff of science fiction but a force shaping the very fabric of modern society.

I must admit that I am not a data scientist, nor do I claim to be a technologist. I am, first and foremost, an English teacher with a curious mind and a penchant for observing how technological shifts reshape the way we live, communicate, and understand the world.

Nevertheless, it is my long-held belief that we educators have a role to play in demystifying AI for the public and our students. It is well known that AI's trajectory, from its modest and somewhat obscure beginnings to its current prominence, has been remarkable.

Let me introduce you to the notion of AI development. Indeed, it has been (made) evolving through distinct stages—each marked by a specific set of capabilities and societal implications. Some argue for a simple binary classification of AI: weak AI and strong AI. Others propose types such as artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial superintelligence (ASI). However challenging, I determine to make it clear that AI, like any other technology, did not appear fully formed but evolved over decades through identifiable phases.

Types of AI and How They Learn

AI is teachable - like us humans. Before I take you through those stages, let’s pause for a moment to explore the types of AI that exist today and the methods through which they learn. I know you would agree with me that understanding these basics makes it easier for us to grasp the bigger picture.

Generative AI: People say this is where AI mimics human creativity. Tools like ChatGPT, DALL·E, and MidJourney have astounded us by generating human-like text, breathtaking images, harmonious music, and even video. My conviction is that generative AI will continue to play a huge role in education, design, and media.

Predictive AI: As we know, predictive AI focuses on forecasting future outcomes based on patterns in data. From movie recommendations to stock market predictions, this type of AI is already a silent partner in our decision-making processes.

Reactive AI: These systems respond to specific inputs without retaining memory of previous interactions. Classic chess-playing programs and early diagnostic tools fall under this category.

Adaptive AI: This is, in my opinion, where things get truly interesting. Adaptive AI not only learns but improves in real-time. It is my personal belief that adaptive AI will be crucial in creating systems that adjust to new conditions and unforeseen challenges.

As for how AI learns, let me briefly outline the training methods. I guess it is safe to say that these learning modes explain much about how AI systems behave.

Supervised Learning: AI is trained on labeled data, essentially learning by example. Think of it as a student being shown flashcards with the correct answers.

Unsupervised Learning: Here, AI discovers patterns and relationships in unlabeled data. I like the idea of AI finding hidden connections we may have overlooked.

Reinforcement Learning: AI learns through trial and error, much like a child navigating a new playground. AlphaGo, which beat the world champion at Go, is a famous example.

The Five Stages of AI Development

Now, let me take you through what I consider the five key stages in AI’s evolution. I must stress that this is a simplified framework meant to give us a clearer perspective on where AI has been and where it might be heading.

Reactive Agents (Chatbots)

Those were the days when everything was simple, but AI was mostly reactive. Early AI systems responded to specific inputs with pre-programmed outputs. ELIZA, developed in the 1960s, was a pioneer. It mimicked a psychotherapist by responding to user inputs with scripted replies. Even today, many customer service bots operate within this reactive framework.

Contextual Agents (Assistants)

As we moved into the 2010s, AI systems began to understand context and learn from interactions. Virtual assistants like Siri, Alexa, and Google Assistant exemplify this stage. They not only respond but anticipate, schedule, and remind. In my opinion, this marked AI’s entry into mainstream consumer life.

Collaborative Agents (Strategists)

By the early 2020s, AI assumed collaborative roles, particularly in fields requiring complex decision-making. Financial analysts, healthcare strategists, and logistics planners started leaning on AI for actionable insights. Some argue against fully trusting AI in strategic roles, but it is well known that human-machine collaboration can outperform human-only teams.

Agentic Entities (Entrepreneurs)

I somehow think it is only a matter of time before AI evolves into agentic entities—fully autonomous systems capable of identifying business opportunities, formulating strategies, and implementing solutions. Experts say these AI entrepreneurs could redefine leadership and management, requiring us to rethink what it means to lead and follow.

Networked Entities (Ecosystem Leaders)

I’d like to entertain you with the idea that, in the final stage, AI systems will operate as part of vast, interconnected networks, collaborating with other AIs and human partners. My gut tells me that this stage will see AI managing entire ecosystems, from global supply chains to health systems and even international diplomacy.

A Brief Timeline: From the 1970s to 2030

It is well known that the journey of AI began with academic curiosity. In the 1970s, systems like ELIZA hinted at the potential of simulated conversation.

The 1980s saw expert systems capable of decision-making based on predefined rules. AI found applications in medicine, engineering, and even legal analysis.

The 1990s and 2000s marked the rise of machine learning. In 1997, IBM’s Deep Blue defeated chess grandmaster Garry Kasparov, signaling AI’s growing prowess.

The 2010s brought deep learning and big data into the limelight. Virtual assistants became household names, while AI made headway in autonomous vehicles and personalized recommendations.

The 2020s and 2030s are projected to witness AI’s transformation into agentic and networked entities. Some argue for strong regulation, while others advocate for innovation-driven policies. I think a balanced approach is crucial.

Ethical Concerns and the Need for Regulation

What we all know and agree upon is that with great power comes great responsibility. AI’s expanding capabilities have triggered ethical concerns around privacy, bias, transparency, and accountability. People say that while AI promises efficiency, it also risks entrenching social inequalities.

Some argue for stringent regulations, citing risks of surveillance, job displacement, and ethical dilemmas in life-or-death decisions. Others contend that overregulation may stifle innovation.

Experts say a nuanced, adaptive regulatory framework is essential. The European Union’s proposed AI Act exemplifies efforts to safeguard rights while promoting responsible AI use.

A Final Thought

In my opinion, AI is neither hype nor a passing trend. It is as real and transformative as the advent of electricity or the internet. No one knows everything, but I would like to emphasize that AI will shape our futures, whether we actively engage with it or not.

I could be wrong, but I sense that AI is not here to replace humanity but to work alongside us. As educators and lifelong learners, we have a duty to foster conversations around AI, demystify its complexities, and prepare future generations to use it wisely.

My conviction is that the future is unfolding before our eyes, and AI will be a pivotal partner in navigating an increasingly intricate world. Having said that, I realize the importance of striking a balance between innovation and caution.

I hope this reflection encourages you to look deeper into AI’s journey and its implications for our lives. Thank you for joining me on this intellectual exploration.

Janpha Thadphoothon is an assistant professor of ELT at the International College, Dhurakij Pundit University in Bangkok, Thailand. Janpha Thadphoothon also holds a certificate of Generative AI with Large Language Models issued by DeepLearning.AI.

Cite as:

Thadphoothon, J. (2025). "The Five Stages of AI Development and What Comes Next" in JT Blog. https://janpha.blogspot.com/2025/04/the-five-stages-of-ai-development-and.html