0:02 AI didn't start with chat GPT or
0:05 self-driving cars. It started with
0:07 something much simpler and honestly kind
0:10 of boring. If then rules. But those
0:12 early steps lay the foundation for
0:14 everything we see today. And if you
0:16 really want to understand where AI is
0:18 going, you have to understand how we got
0:21 here stage by stage. Let's break it
0:24 down. Stage one, rules-based systems.
0:27 Back in the 1950s and60s, the dream of
0:30 building a thinking machine was already
0:32 alive. Researchers weren't training
0:34 models on billions of data points. They
0:36 were writing rules. Literally lines of
0:40 code that said, "If X happens, do Y."
0:42 One of the most famous early programs
0:46 was Eliza, created in 1966 by Joseph
0:50 Weisenbomb at MIT. Eliza mimicked a
0:53 psychotherapist by rephrasing user input
0:55 as questions. If you typed, "I'm feeling
0:59 sad," Eliza might reply, "why are you
1:01 feeling sad?" It wasn't smart. It didn't
1:03 understand emotions, but it fooled
1:05 people into thinking it did. That was
1:07 one of the first realworld moments where
1:10 humans projected intelligence onto a
1:13 machine. By the 1980s, these rules-based
1:16 systems evolved into what were called
1:18 expert systems. Companies like Digital
1:21 Equipment Corporation used them to
1:23 troubleshoot hardware. Medical expert
1:25 systems like M were built to suggest
1:28 treatments for infections. For a while,
1:31 businesses believed expert systems were
1:33 the future. In fact, by the mid80s, the
1:36 market for expert systems was valued in
1:38 the billions. But here's the catch.
1:40 These systems were brittle. If you gave
1:43 them a situation outside their rules
1:45 they broke. Imagine trying to write down
1:47 every possible medical scenario or
1:49 troubleshooting step. It just didn't
1:51 scale. That's why expert systems peaked
1:54 in the late8s and collapsed by the early
1:56 '90s. What replaced them was something
1:58 much more powerful. Machines that didn't
2:01 just follow rules, but actually learned
2:04 patterns from data. Stage two, machine
2:07 learning emerges. This is where AI took
2:09 a sharp turn. Instead of telling the
2:12 computer exactly what to do, researchers
2:14 started teaching it how to learn from
2:17 examples. By the 1990s, machine learning
2:19 became the dominant approach. A classic
2:21 example, spam filters. Instead of
2:24 writing endless rules like if the email
2:26 contains the word lottery, mark it as
2:29 spam. Machine learning algorithms looked
2:31 at thousands of real emails and figured
2:33 out the statistical patterns that
2:35 separated spam from legitimate mail.
2:38 This was a huge leap because the system
2:41 could adapt as spammers changed tactics.
2:44 Recommendation engines also came out of
2:46 this era. When Amazon introduced its
2:49 customers who bought this also bought
2:51 feature in the late '9s, it wasn't
2:54 random. It was powered by collaborative
2:56 filtering, a machine learning technique
2:58 that studied user behavior to predict
3:00 what else you might like. That
3:02 recommendation system is still a
3:04 cornerstone of Amazon's business today.
3:07 Around the same time, speech recognition
3:10 started making progress. In 1997, Dragon
3:13 Naturally Speaking launched as the first
3:15 large vocabulary continuous speech
3:18 recognition software for consumers. It
3:20 required training, but it was a glimpse
3:22 of what was possible when you combined
3:24 machine learning with computing power.
3:26 The limitation though was scale. These
3:28 early models couldn't handle massive
3:30 amounts of data. They were narrow, good
3:33 at one task, bad at everything else. But
3:35 they opened the door to the next stage
3:38 when data, algorithms, and computing
3:41 power collided and pushed AI into
3:44 territory no one thought possible. Stage
3:47 three, deep learning revolution. If
3:48 machine learning was about teaching
3:50 computers to learn from data, deep
3:52 learning was about giving them a
3:54 brain-like architecture to learn at
3:57 scale. The term deep comes from deep
4:00 neural networks, layers upon layers of
4:02 artificial neurons that process data in
4:04 stages. The breakthrough moment came in
4:08 2012. A team led by Joffrey Hinton at
4:10 the University of Toronto entered the
4:13 imageet competition, a massive challenge
4:15 to classify images into categories.
4:17 Their deep neural network crushed the
4:20 competition, cutting error rates by more
4:23 than 40%. That single win is often
4:25 marked as the birth of modern deep
4:28 learning. Suddenly, AI wasn't just
4:30 recognizing patterns in spreadsheets. It
4:33 was identifying cats and YouTube videos.
4:35 In fact, in 2012, Google trained a
4:38 neural network on 10 million random
4:40 YouTube thumbnails. And without any
4:42 labels, the system learned to recognize
4:45 cats on its own. That was a moment of
4:47 cultural buzz. the idea that machines
4:49 could teach themselves concepts humans
4:52 never explicitly programmed. From there,
4:54 the dominoes started falling. Speech
4:57 recognition systems like Siri and Google
5:00 Voice went from frustrating to actually
5:04 usable around 2014 to 2016 because of
5:06 deep learning. Translation took off with
5:08 Google Translate's neural machine
5:11 translation update in 2016, which made
5:14 translations far more fluent. Then came
5:18 Alph Go in 2016. Built by DeepMind, it
5:20 defeated world champion Lee Sadull in
5:23 the ancient game of Go, a game
5:25 considered far too complex for brute
5:27 force computing. That wasn't just a win
5:30 in a board game. It showed that AI could
5:33 master strategic reasoning and long-term
5:35 planning in ways most researchers didn't
5:38 expect to see so soon. The 2010s were
5:40 the decade of deep learning dominance,
5:42 but deep learning still had one catch.
5:44 It was narrow. These systems were
5:46 incredibly strong in their own lanes.
5:49 Chess, go, image recognition, speech,
5:51 but they didn't generalize. They
5:53 couldn't step outside their trained
5:56 domain. That set the stage for what came
5:59 next. Foundation models that could learn
6:03 once and apply across countless tasks.
6:06 Stage four, generative AI and foundation
6:09 models. Around 2018, AI entered a new
6:12 stage, one that directly shapes our
6:14 daily lives today. Instead of training a
6:17 separate system for every single task,
6:19 researchers built foundation models,
6:22 huge neural networks trained on vast
6:24 amounts of general data from books to
6:27 websites to images. The results were
6:30 startling. In 2019, Open AI released
6:33 GPT2, a model that could generate
6:35 paragraphs of text that sounded
6:39 coherent. By 2020, GPT3 pushed this to
6:44 the extreme with 175 billion parameters,
6:46 showing a single model could handle
6:49 translation, summarization, Q&A, and
6:51 even codewriting. It wasn't perfect, but
6:54 it was versatile. A major leap from
6:56 narrow AI. It wasn't just text.
7:00 Generative AI spread across modalities.
7:03 DL E 2021 could generate images from
7:08 props. Stable diffusion 2022 made AI art
7:10 open-source, sparking a wave of
7:13 creativity and controversy. Midjourney
7:16 became a cultural phenomenon with AI
7:19 generated art flooding social media. In
7:21 parallel, text to video and text to
7:25 audio models emerged. In 2023, Runway
7:28 Gen 2 and Pika Labs showed AI could
7:31 generate short video clips from text.
7:34 OpenAI released Whisper in 2022, a
7:36 state-of-the-art speech recognition
7:39 model, and in 2024 unveiled Sora,
7:42 demonstrating realistic long- form AI
7:46 video generation. By 2023 and 2024,
7:48 generative AI wasn't just an experiment.
7:52 It was integrated into mainstream tools.
7:55 Microsoft embedded GPT into Office as
7:57 co-pilot. Google launched Gemini to
7:59 compete head-to-head. Adobe added
8:02 Firefly AI directly into Photoshop.
8:05 Millions of people now use generative AI
8:08 every day, from writing emails to
8:10 creating art. But here's the hook. These
8:13 models don't understand the world. They
8:15 predict patterns based on training data.
8:17 That's why they sometimes hallucinate
8:20 facts or make reasoning errors. They're
8:22 powerful, but they're not yet general
8:25 intelligence. The leap from generating
8:28 to acting, from responding to initiating
8:31 is what defines the next stage,
8:35 autonomous agents. Step five, autonomous
8:38 agents. If generative AI was about
8:41 producing text, images, and videos on
8:43 demand, autonomous agents are about
8:46 taking action. These systems don't just
8:49 answer prompts. They plan, decide, and
8:52 execute tasks across multiple steps. In
8:57 2023, tools like AutoGPT and Baby AGI
8:59 grabbed headlines. They chain together
9:01 multiple uses of large language models
9:04 to set goals and carry them out with
9:08 little human input. For example, AutoGPT
9:10 could be told, "Research the best
9:12 headphones under $200 and compile them
9:15 into a report." And it would run web
9:17 searches, analyze results, and generate
9:19 a document. More recently, companies
9:22 have pushed this idea further. In 2024,
9:25 Cognition AI unveiled Devon, marketed as
9:28 the first AI software engineer. Devon
9:31 could take a highlevel instruction like
9:33 build a site with a loon system and
9:35 handle the coding, debugging, and
9:38 deployment steps. While not flawless, it
9:40 highlighted how agents move us from
9:42 assistants that help to systems that
9:45 work. We're also seeing agents deployed
9:47 in customer service, finance, and even
9:50 robotics. Autonomous AI call center
9:53 agents are already being piloted by
9:55 startups and enterprise firms to handle
9:58 real customer interactions without human
10:00 handoff. In robotics, labs are
10:02 experimenting with giving language
10:05 models control over realworld robots,
10:07 connecting reasoning with physical
10:10 action. But this autonomy raises new
10:12 challenges. Who's accountable if an
10:14 agent makes a bad decision? How do we
10:17 keep them aligned with human intent?
10:19 These questions tie directly to the next
10:23 stage in the evolution of AI. Stage six,
10:26 artificial general intelligence, AGI.
10:29 AGI is the point where AI systems can
10:31 perform a wide range of intellectual
10:34 tasks at human level or beyond. Unlike
10:38 narrow AI, good at one task, AGI would
10:41 be flexible, adaptive, and capable of
10:43 transferring knowledge across domains.
10:45 Tech leaders openly debate how close we
10:49 are. Sam Alman of Open AI has said AGI
10:51 could arrive within the next decade,
10:53 while others argue today's large models
10:56 are still missing core ingredients like
10:58 reasoning and true understanding.
11:00 Joffrey Hinton, one of the godfathers of
11:04 AI, left Google in 2023, partly to speak
11:07 freely about the risks of advanced AI,
11:09 warning that systems may learn faster
11:11 than expected. We're seeing steps in
11:14 that direction with multimodal models.
11:17 AI that can process text, images, audio,
11:20 and video together. Google's Gemini and
11:24 OpenAI's GPT5 demos in 2024 highlighted
11:27 reasoning across multiple formats, but
11:29 whether scaling these models leads
11:31 directly to AGI or whether new
11:33 breakthroughs are required remains an
11:36 open question. For now, AGI is still a
11:39 research goal, not a present reality.
11:42 But what lies beyond AGI is even more
11:45 transformative and more controversial.
11:47 Stage seven, artificial super
11:51 intelligence, ASI. ASI is the
11:54 hypothetical stage where AI surpasses
11:56 human intelligence in every domain. If
12:01 AGI is human level, ASI is beyond human.
12:03 It could solve problems humans can't.
12:05 From drug discovery to climate modeling
12:08 at speeds we can't match. This isn't
12:10 science fiction. It's a logical
12:12 extrapolation. If machines reach human
12:14 level intelligence and they can
12:17 self-improve, they may rapidly surpass
12:20 us. That's why governments and companies
12:22 are already talking about guard rails.
12:26 In 2023, the US and EU began drafting AI
12:29 governance frameworks. By 2024, over 20
12:32 countries signed agreements focused on
12:34 AI safety and transparency. The
12:38 opportunity is enormous, but so are the
12:40 risks. Researchers discuss alignment,
12:43 the challenge of making sure AI goals
12:45 stay compatible with human goals. This
12:48 is one of the most active areas of AI
12:51 research today. So, if we step back, the
12:53 path becomes clear. We started with
12:55 rules-based systems in the 1960s, moved
12:58 into machine learning in the 1990s, hit
13:00 the deep learning revolution in the
13:03 2010s, shifted to generative AI and
13:06 foundation models in the 2020s. Now
13:08 we're entering the world of autonomous
13:12 agents. With AGI on the horizon and ASI
13:15 as a possible future, each stage didn't
13:17 erase the last, it built on it. The
13:19 brittle rules of the 60s taught us
13:21 structure. Machine learning gave us
13:23 adaptability. Deep learning gave us
13:27 scale. Generative AI gave us creativity.
13:29 And agents are giving us autonomy. The
13:32 question isn't whether AI will keep
13:35 evolving. It's how fast and in what
13:37 direction. Because if history tells us
13:40 anything, it's that the next leap often
13:42 comes sooner than anyone expects. If
13:44 you've made it this far, let us know
13:46 what you think in the comment section
13:48 below. For more interesting topics, make
13:50 sure you watch the recommended video
13:52 that you see on the screen right now.
