The AI Podcast

Episode: The History of AI
Host: Jaden Schaefer
Date: February 2, 2026

Overview

In this episode, Jaden Schaefer embarks on a fascinating exploration of the history of artificial intelligence, tracing the journey from its philosophical beginnings in the mid-20th century to today’s transformative technologies. Schaefer breaks down key milestones, setbacks, and paradigm shifts, discussing how AI evolved from symbolic logic to data-driven machine learning and the ‘modern AI boom’. With an accessible tone and engaging storytelling, the episode situates today’s advancements in a wider historical context—shedding light on how past aspirations and pitfalls have shaped the present landscape.

Key Discussion Points & Insights

1. Early Foundations: Pre-Computing & the Birth of AI (01:13–04:00)

• Origins Before Computers:

  • Schaefer notes that the concept of artificial intelligence predates powerful computers. Visionaries in the 1940s and 50s speculated:

    “Can machines think?” (01:35)

  • At this time, computers were massive and only capable of basic calculations.

• Logic and Math as Intelligence:

  • The early foundational belief:

    “If human reasoning followed rules, then ... you could encode those rules into a machine. And that was basically the foundational belief of the early AI.” (02:25)

• Official Birth of AI:

  • The term 'Artificial Intelligence' was coined at a historic 1956 workshop, now regarded as the field’s official beginning.

2. Symbolic AI: Optimism and the First AI Winter (04:05–08:40)

• Early Optimism:

  • Researchers in the 1950s/60s predicted human-like intelligence was only 20 years away, believing that tasks like vision and language were essentially solved.

    “I think it was wildly optimistic ... The spoiler alert is that they were not.” (03:45)

• Symbolic Approaches (Rule-based):

  • Systems were built on hand-coded “if-this-then-that” rules, excelling in small domains like chess but failing in complex, real-world contexts.

• Disillusion and Funding Cuts:

  • The limits of symbolic AI led to broad disillusionment:

    “No matter how many rules you write, you never can actually capture everything that happens in reality.” (07:10)

  • Widespread funding dried up—ushering in the first AI Winter.

3. Expert Systems and Second AI Winter (08:40–12:00)

• Resurgence via Expert Systems (1980s):

  • AI returned with the development of “expert systems” designed for specialized decision-making.

    “Companies poured a ton of money into those systems because ... in really narrow domains, they actually worked quite well.” (09:30)

• Challenges Remain:

  • High cost of building and maintaining rules, inability to adapt to a changing world, brittleness.

    “Every time the world changed, you had to update the rules manually ... everyone gets disappointed and then you get another AI Winter.” (10:25)

4. Machine Learning & the Dawn of Modern AI (12:00–18:00)

• Shift to Machine Learning:

  • The approach moves from explicit rules to letting computers learn from data, inspired by the human brain’s neurons.

• Neural Networks: Early Attempts and Dormancy:

  • Concepts as early as the 1950s, but hampered by weak hardware, little data, and complex math.

• The Big Breakthroughs:

  • Data Explosion: “...the Internet, smartphones, social media, so much data is being created.” (13:48)
  • Cheaper, Better Compute: “GPUs, originally built for gaming, turned out to be really perfect for training neural networks.” (14:30)
  • Deep Learning Breakthroughs: “By ... adding all of that, the data, the compute, and that new strategy, everything changed.” (15:10)

• Deep Learning Crushes Benchmarks (2010s):

  • AI systems surpass previous efforts in image recognition, speech, and language translation.

    “Image recognition ... it actually worked. Speech recognition got really good. Translations ... went from being super terrible to usable.” (16:00)

5. The Modern AI Boom: Scaling Up (18:00–23:00)

• Exponential Scaling:

  • “...the more data you give them, the better they got. The more compute you give them, the smarter they become.” (17:00)
  • Models and datasets balloon, leading to present-day large language models.

• Reality vs Hype:

  • Despite progress, current AIs are not sentient or conscious:

    “They don’t have consciousness ... What they have is this kind of statistical understanding of patterns in data and ... massive scale.” (18:30) “Once you scale that far enough, you start getting behavior that looks a lot like reasoning, but it's still just pattern recognition and prediction.” (19:20)

6. A New Reality: Lasting Impact and Future Prospects (23:00–end)

• Real World Value:

  • AI now delivers genuine economic impact—transforming work, research, business, and creativity.

    “We are seeing these systems actually work. They're actually transforming how people code, write, design, build businesses ... AI is actually helping us.” (20:10)

• Democratization:

  • The tools are becoming cheaper, faster, and accessible—empowering individuals rather than just massive corporations.

• Technological Progress Patterns:

  “Every technological shift ... followed the same pattern: early hype, disappointment, slow progress, and then all of a sudden, everything clicks. I think that's where we are now with AI.” (21:00)

• Patience and Perspective:

  “It took many decades of all these different ideas failing before we were able to be successful ... The payoff right now is massive.” (22:00) “We're getting to a world where intelligence is becoming more of a commodity ... intelligence is going to get a lot cheaper and abundant.” (22:35)

• Conclusion:

  • The AI journey did not happen overnight—it’s the result of decades of work, setbacks, and perseverance.

    “It's definitely been a very long road since the 40s and 50s, but now ... now that all this AI is here, I don't think it's going away.” (23:00) “We're heading into one of the most exciting periods of innovation that we've ever seen. And so I'm super excited to go on this journey.” (23:25)

Notable Quotes

• On Early AI Aspirations:

  “If human reasoning followed rules, then ... you could encode those rules into a machine. And that was basically the foundational belief of the early AI.” — Jaden Schaefer (02:25)

• On Symbolic AI’s Limitations:

  “No matter how many rules you write, you never can actually capture everything that happens in reality.” — Jaden Schaefer (07:10)

• On The Impact of Deep Learning:

  “By ... adding all of that, the data, the compute, and that new strategy, everything changed.” — Jaden Schaefer (15:10)

• On Modern AI’s True Nature:

  “They don’t have consciousness ... What they have is this kind of statistical understanding of patterns in data and ... massive scale.” — Jaden Schaefer (18:30)

• On Technological Shifts:

  “Every technological shift ... early hype, disappointment, slow progress, and then all of a sudden, everything clicks. I think that's where we are now with AI.” — Jaden Schaefer (21:00)

Key Timestamps

• 01:13 – The seeds of AI in 1940s-50s thinker’s visions
• 02:25 – Reducing human reasoning to logic and rules
• 04:00 – The symbolic AI era and its optimism
• 07:10 – Rule-based AI and the first AI winter
• 09:30 – The 1980s rise of expert systems
• 11:45 – Second AI winter
• 13:48 – Big Data era and pivotal advances
• 15:10 – Deep learning breakthroughs
• 16:00 – Machine learning starts outperforming prior benchmarks
• 17:00 – Modern AI scaling and dataset explosion
• 18:30 – What today’s AI is—and isn’t
• 20:10 – Economic transformation and value of AI
• 21:00 – Repeated cycle of hype and disappointment; the inflection point
• 22:35 – Intelligence as an abundant, cheap commodity
• 23:25 – Entering an era of unprecedented innovation

Tone & Style

• Engaging, accessible, and insightful, striking a balance between storytelling and technical clarity.
• Frequent use of metaphors and real-world analogies to demystify complex ideas:

  “We’ve all seen the pictures of these computers ... the size of a room ... back then.”

• Reflective and optimistic about AI’s potential and current trajectory.

Final Thoughts

Jaden Schaefer delivers a compelling, richly contextualized history of AI, highlighting both the “wildly optimistic” beginnings and the real, durable value seen in today’s systems. By grounding modern developments in their historical trajectory, the episode provides nuanced perspective for enthusiasts and professionals alike—reminding listeners that the road to transformative innovation is long, full of pitfalls, yet ultimately rewarding.