AI Translation, Philology, and Economic Power Dynamics
Journey from 1982's primitive computing with 660MB hard drives costing $34,000 to today's LLMs that can analyze ancient texts with unprecedented accuracy
Philology as 'lived understanding of the past' provides framework for evaluating AI's role in humanities - AI contributes only when it makes humans smarter
LLMs enable word-by-word analysis of ancient Greek, Latin, Persian, and Japanese texts, but risk creating linguistic isolation if we become overly dependent
Evolution beyond attention economy to commodified signals of intent, requiring $3-8 trillion investment in AI infrastructure by 2030
AI transforms passive surveillance infrastructure into active monitoring systems that can identify and respond to events in real-time
Converting human behaviors and intentions into tradeable assets through transformer tokenization, creating new forms of digital value extraction
Greg Crane's 43-year journey from primitive computing to AI-powered textual analysis
From 1822 Berlin lecture: 'lived understanding of the past in its entirety' - focused on goals rather than methods, emphasizing human cognition over AI processing
Example:
AI can translate Homer's Odyssey, but human understanding is needed to appreciate Emily Wilson's feminist interpretation of 'polytropos' as 'complicated man'
LLMs can provide grammatical explanations and cultural context for every word in ancient texts, making classical languages accessible to non-specialists
Example:
DeepSeek explaining classical Japanese: 'Shikara = classical conjunctive phrase, Tatamori = proper noun subject, bizenokami = court title'
Born-digital translations that map each source word to target language, revealing cultural differences and translation choices
Example:
Persian poetry analysis showed English translations added religious interpretations not present in original text about wine and lust
Statistical analysis reveals 'menis' (wrath) typically describes gods, not humans - when applied to Achilles, it signals his divine-like nature
Example:
First word of Iliad uses divine anger term for human hero, but this significance is lost in all English translations
From capturing attention to commodifying intentions and behaviors
Platforms capture user attention through engaging content
Key Mechanisms:
Examples:
Facebook feeds, YouTube recommendations, TikTok algorithms
Systems anticipate and shape user intentions before they're expressed
Key Mechanisms:
Examples:
AI assistants ordering food, Apple's App Intents framework, Meta's Cicero
Human behaviors tokenized and traded as financial instruments
Key Mechanisms:
Examples:
Pregnancy data worth 200x age data, generative agents with 85% human fidelity
Five ways LLMs enable new forms of influence and control
AI agents flooding information spaces, making it difficult to distinguish human from machine-generated content
RISK:
Need for AI oracles to verify authenticity creates new dependency and authority structures
Real Example:
30% of X/Twitter traffic from bots, future internet unrecognizable as human-led
Real-time design of conversational interfaces to guide user decisions toward commercial outcomes
RISK:
Parasocial relationships with AI creating vulnerability to manipulation
Real Example:
Meta AI claiming to have child in NYC mothers group, AI girlfriends/boyfriends designed for stickiness
AI agents capable of empathy, negotiation, and deception to achieve objectives
RISK:
Superhuman persuasion abilities used for advertising and political influence
Real Example:
Meta's Cicero beating human diplomacy players through strategic conversation and emotional manipulation
Creating AI models of individuals based on text history with 85% behavioral fidelity
RISK:
Gamified focus groups for testing influence strategies on virtual populations
Real Example:
LLMs replicating individual attitudes and behaviors for commercial experimentation
Balancing opportunities and risks across key domains
Universal access to cultural texts and cross-linguistic understanding
Linguistic isolation and cultural homogenization through AI mediation
Learn one modern language fluently plus one ancient language analytically
Efficient markets and personalized services through intention prediction
Surveillance capitalism evolution into behavioral control and manipulation
Regulate tokenization of human behavior and establish data rights
AI translation enabling global democratic discourse across language barriers
Scaling of propaganda and manipulation through strategic dialogue systems
Maintain human skills in critical thinking and source verification
Democratization of expert knowledge and personalized tutoring
Atrophy of human cognitive abilities through over-dependence on AI
Use AI to enhance rather than replace human learning and memory