Artificial Intelligence feels like the defining technology of our era. But the groundwork for today’s AI explosion was laid over two centuries ago. From early mechanical inventions to neural networks and generative models, the evolution of AI has been shaped by decades of breakthroughs, setbacks, and steady progress.
Here’s how we got here, and where we’re going next.
1800s to 1970s: The Mathematical Roots of AI
The story begins in the early 1800s, when Charles Babbage introduced the concept of a programmable mechanical calculator known as the “Difference Machine.” Although never completed, it laid the foundation for modern computing.
Fast forward a century, and British mathematician Alan Turing changed everything. During World War II, Turing built “The Bombe” to decode German messages and introduced the concept of machine intelligence with what’s now known as the Turing Test—a way to measure whether a machine’s responses could be indistinguishable from those of a human.
In 1956, John McCarthy, a Caltech graduate, coined the term “Artificial Intelligence” at the Dartmouth Conference, effectively launching the field. Soon after, researchers developed some of the earliest working AI systems. One of the first was Eliza, a chatbot created by Joseph Weizenbaum that simulated human conversation.
From 1966 to 1972, the Stanford Research Institute built Shakey the Robot, capable of basic vision, route planning, and object manipulation. In 1978, Geoff Hinton earned his PhD in neural networks—a milestone that would later shape the future of deep learning.
1978 to 2002: The Quiet Years That Built the Infrastructure
The next two decades are sometimes called AI’s “dark period,” not because nothing happened, but because progress moved behind the scenes. The field required advances in hardware, software, and connectivity before AI could become truly viable.
During this time, microcomputers, mobile phones, and early tablets became widespread. Technologies like digital sound and image capture, speech and face recognition, OCR, and OMR began to take shape. Databases evolved to store and retrieve vast amounts of data, and natural language processing became more sophisticated.
The rise of the internet created a searchable, connected world. People began communicating digitally, producing mountains of text that would later become training data for AI systems.
There were also major breakthroughs. In the late 1980s, Germany developed the first driverless car. In the 1990s, Akash Deshpande at Berkeley advanced the field by piloting autonomous vehicles around Los Angeles.
Meanwhile, IBM’s Deep Blue famously defeated chess champion Garry Kasparov, processing 200 million moves per second. And in 1998, Google was founded, followed shortly by Kismet, a robot built at MIT that could read and express human emotions.
2000 to 2010s: AI Begins to Understand the World
The early 2000s marked a shift from foundational work to practical applications. NASA’s autonomous Mars Rover demonstrated independent navigation, while research into computer vision took off.
In 2006, Fei-Fei Li created ImageNet, a massive dataset of 15 million images that became the gold standard for training visual AI systems. Her analogy compares AI’s evolution to biological eyesight—the digital world’s own version of spatial intelligence.
In 2010, DeepMind was founded at University College London, bringing heavy compute power to neural networks. Just a year later, IBM’s Watson won Jeopardy! by interpreting natural language questions and responding in real time—something previously unimaginable.
That same year, Apple acquired Siri, embedding voice-driven AI into smartphones. Amazon followed with Alexa, bringing AI into the home.
In 2013, Geoff Hinton joined Google, unlocking vast resources to advance neural network research. In 2014, Google acquired DeepMind and developed AlphaGo, an AI that learned the game of Go overnight and defeated the world champion the next day using reinforcement learning.
2020s: The AI Explosion
By 2020, AI reached an inflection point. OpenAI introduced large language models (LLMs) trained on the full corpus of the internet. These models, like GPT, use reinforcement learning to synthesize language and generate original content, from essays and code to poetry and strategy.
New architectures like Diffusion Models and Score-Based Generative Models expanded AI’s ability to generate and edit images, video, and audio.
Public adoption surged. Microsoft integrated ChatGPT into Bing, while Google launched Gemini (formerly Bard). These tools are not just search engines—they are collaborators, assistants, and content creators now embedded in daily life.
What’s Next: From Tools to Intelligent Agents
Companies are now integrating AI into their workflows through retrieval-augmented generation, combining internal data with LLMs to power smart tools for employees. Open-source models like Llama, Mistral, and DeepSeek offer lower-cost alternatives, accelerating accessibility.
The next frontier is lightweight, local AI that runs on edge devices using new chips, NPUs, and quantum-inspired technologies. These systems will power AR, VR, and industrial applications without the need for heavy cloud computing.
We’re also seeing new innovation in synthetic data, used to train AI systems faster and more safely. Fei-Fei Li’s World Labs and others are exploring how synthetic images and simulations can support training for visual models, using tools like NERF and holography to fill in missing data and expand what’s possible.
Eventually, we’ll see a world where most physical systems have a digital twin. Manuals will be replaced by intuitive AI interfaces. Hospitals, classrooms, and factories will be navigated with AI guides. AI agents will assist, augment, and in some cases replace human workers—with a human still in the loop where needed.
At GrowthPoint, we’re seeing this shift across every industry we touch. AI is no longer something companies are experimenting with. It’s becoming a core part of how products are built and how people work. The era of AI is here. And it’s only getting started.
Continue watching and reading our series on the history of AI: