Markov chains are the original language models

Nostalgia and early text bots

• Many recall early Markov-based chatbots (MegaHAL, IRC/Slack/Skype/Minecraft bots, Babble!, Reddit simulators) that mimicked users or communities with amusing but often deranged output.
• These systems were used for pranks, “away” bots, or playful conversation, and often produced text that sounded like someone on the verge of a breakdown.
• Markov text generators also powered joke sites (e.g., postmodern essay generators, KingJamesProgramming-style mashups) and early “AI” experiments like Racter.

Markov chains in text generation and spam

• Before modern ML, Markov chains were standard for auto-generated text, SEO spam, and nonsensical keyword pages that fooled early search engines.
• Commenters note that Markov states need not be single words; n‑grams and skip-grams are common, with smoothing (e.g., Laplace) needed to handle unseen transitions.
• Simple code examples show how tiny scripts can produce surprisingly coherent pseudo‑biblical or pseudo-man-page prose.

Technical limitations of classical Markov models

• Key limitation: linear, local context. With only current state (or short n‑gram) visible, they miss long-range or non-linear structure (e.g., 2D images with vertical patterns, complex language dependencies).
• Trying to encode longer dependencies via higher-order Markov models causes exponential state blowup (e.g., needing 2^32 states to link two pixels separated by 32 random bits).
• Some mention techniques like skip-grams and more complex mixtures, but overall see Markov models as quickly becoming impractical for rich structure.

Debate: Are LLMs “just” Markov chains?

• One camp: decoder-only LLMs are Markov processes if you treat the entire context window as the current state; attention just gives a richer state representation, not a different probabilistic structure.
• Others argue this is technically true but practically unhelpful: if you let “state” be arbitrarily large, almost any computation becomes Markovian, so the label stops offering insight.
• Several warn that “LLMs are just fancy Markov chains” leads people to underestimate their capability and societal impact, conflating simple n‑gram models with high-dimensional transformer models.
• There’s discussion about finite context windows, tool use, memory-augmented models, and the boundary between Markovian and non-Markovian behavior, with no full consensus.

Pedagogical value and mental models

• Many see Markov chains as an excellent teaching tool: easy to implement, good for explaining next-token prediction, temperature/logit sampling, and for motivating why attention and neural nets are needed.
• Others caution that oversimplified analogies should not be used to reason about detailed LLM behavior or long-term AI risks.

Resources and tooling

• Numerous references are shared: classic books and papers (Shannon, Rabiner, early neural language models), historical bots and generators, Perl/ Python toy implementations, educational Markov visualizers, and CPAN tools like Hailo.