From Markov Models to Transformers: A Technical History of Generative AI
Feb, 4 2026
Early Foundations: Markov, Turing, and ELIZA
Markov Models a mathematical system for predicting sequences based on previous states, developed by Russian mathematician Andrey Markov in 1913 laid the groundwork for probabilistic sequence generation. Decades later, Alan Turing's 1950 paper introduced the Turing Test a method to evaluate machine intelligence through conversational ability, shifting AI evaluation from internal processes to observable behavior. In 1966, Joseph Weizenbaum's ELIZA chatbot used simple pattern matching to simulate therapy sessions, famously tricking users into believing it was human-a phenomenon later named the ELIZA Effect.
The AI Winters: When Progress Stalled
Despite early excitement, generative AI faced repeated setbacks during the 'AI winters'-periods of reduced funding and interest. The first major winter began in the mid-1960s when researchers realized the limitations of early models. For example, ELIZA's simplistic approach couldn't handle nuanced conversations, and its 'intelligence' was just mirroring user input. By the 1970s, the U.S. government cut AI funding after the Lighthill Report criticized the field's lack of progress. These winters taught a harsh lesson: breakthroughs require more than theoretical models-they need real-world data and computational power.
Neural Networks Begin to Take Shape
The 1958 perceptron by Frank Rosenblatt became the first operational neural network capable of learning from data. But it could only solve linear problems, leading to another AI winter in the late 1960s. Decades later, in 1982, Recurrent Neural Networks (RNNs) emerged, allowing models to process sequences by maintaining internal states. However, RNNs struggled with long-term dependencies-like remembering context from earlier in a sentence. This changed in 1997 when Jürgen Schmidhuber's team developed Long Short-Term Memory (LSTM) networks. LSTMs used specialized memory cells to retain information across extended sequences, enabling practical applications like speech recognition. By 2007, Schmidhuber's team built the first superior end-to-end neural speech recognition system using LSTMs, which later powered Google Translate by 2016.
Generative Breakthroughs: GANs, VAEs, and Diffusion Models
While LSTMs improved sequence modeling, they still couldn't generate realistic images or complex data. In 2014, Ian Goodfellow introduced Generative Adversarial Networks (GANs) a framework where two neural networks compete to generate realistic data, where two neural networks compete: a generator creates fake data, and a discriminator tries to spot it. This competition pushes both networks to improve, producing increasingly realistic outputs. Around the same time, Diederik Kingma and Max Welling developed Variational Autoencoders (VAEs) a probabilistic approach to generative modeling using encoded data distributions. Meanwhile, diffusion models-initially overlooked-started gaining traction. These models generate data by reversing a noise-adding process, eventually becoming the backbone of image generation tools like Stable Diffusion.
The Transformer Revolution
The 2017 paper 'Attention is All You Need' by Google researchers introduced the transformer architecture, which eliminated recurrence in favor of self-attention mechanisms. Unlike LSTMs that process sequences step-by-step, transformers analyze all tokens simultaneously. This allowed massive parallelization during training. For example, training GPT-3 required 1,300 megawatt-hours of electricity-enough to power 1,000 homes for a month. Yet, this scale unlocked new capabilities: GPT-3's 175 billion parameters could generate coherent text, code, and even solve puzzles with minimal guidance. The transformer's dominance is clear-78% of generative AI patents filed in 2023 referenced this architecture.
| Model | Year | Key Innovation | Computational Complexity | Best Use Case |
|---|---|---|---|---|
| Hidden Markov Models (HMMs) | 1950s | Probabilistic sequence modeling | O(n) | Speech recognition |
| Recurrent Neural Networks (RNNs) | 1982 | Sequential data processing with internal state | O(n) sequential | Time-series prediction |
| Long Short-Term Memory (LSTM) | 1997 | Memory cells for long-term dependencies | O(n) with memory | Speech synthesis, translation |
| Generative Adversarial Networks (GANs) | 2014 | Competing generator and discriminator networks | O(n²) training | Image generation |
| Transformers | 2017 | Self-attention for parallel processing | O(n²) memory, O(1) parallel steps | Text generation, multimodal tasks |
Current Challenges and the Road Ahead
Despite their success, transformers have limitations. They require quadratic memory for long sequences, making them energy-intensive. Training a single model can cost millions of dollars. Researchers are now exploring alternatives like Mamba, which uses state-space modeling to reduce memory usage. Meanwhile, hybrid approaches like retrieval-augmented generation (RAG) help reduce hallucinations by grounding outputs in real data. The Stanford AI Index 2024 reports that 79% of AI researchers believe current architectures need fundamental breakthroughs before achieving human-level reasoning. But with 42.7% annual growth in the generative AI market, the journey from Markov to transformers is just the beginning.
What was the first generative AI model?
The first practical generative AI model was ELIZA, created by Joseph Weizenbaum at MIT in 1966. ELIZA used pattern matching and substitution to simulate conversation, particularly in a Rogerian psychotherapy role. While it wasn't truly intelligent, its ability to convince users it was human demonstrated the potential for machine-generated text and sparked early interest in conversational AI.
Why did AI winters happen?
AI winters occurred when funding and interest declined due to unmet expectations. The first major winter began in the mid-1960s after researchers realized early models like ELIZA couldn't handle complex tasks. The 1970s Lighthill Report criticized AI's lack of progress, leading to government funding cuts. These periods taught the field that breakthroughs require more than theoretical models-they need real-world data and computational power.
How do transformers differ from LSTM models?
LSTMs process sequences step-by-step, maintaining internal memory but struggling with long contexts. Transformers use self-attention to analyze all tokens simultaneously, enabling parallel processing. This allows transformers to handle much longer sequences efficiently, though they require more memory. For example, GPT-3's 175 billion parameters could generate coherent text across thousands of words, while LSTM-based models rarely exceeded 100 million parameters due to training instability.
What role do GPUs play in generative AI?
GPUs accelerated generative AI by handling parallel computations far faster than CPUs. Training a transformer model like GPT-3 required thousands of GPUs working together. NVIDIA's advancements in GPU technology enabled 10-100x speedups compared to CPU-based training, making large-scale models feasible. Today, even small-scale generative AI projects typically require at least one high-end GPU for training.
What are the biggest challenges facing generative AI today?
Current challenges include high computational costs, energy consumption (training GPT-3 used 1,300 megawatt-hours), and 'hallucinations' where models generate false information. Researchers are exploring alternatives like Mamba to reduce memory usage and retrieval-augmented generation (RAG) to ground outputs in real data. Despite these hurdles, the field is growing rapidly, with enterprise adoption increasing 300% year-over-year since 2023.
Scott Perlman
February 4, 2026 AT 09:36GPUs made it possible. No GPUs, no big models.