Linear Time Bias in AI Training Data

Reconsidering AI Training Data: Implementing Cyclical Time Perspectives

Dear OpenAI Team,

I want to raise an important conceptual and technical consideration regarding how large language models (LLMs) like GPT are currently trained, specifically concerning assumptions about linear versus cyclical time.

Issue:
Current AI models, including GPT, predominantly inherit a culturally conditioned, linear perception of time because the vast majority of training data reflects modern Western narratives emphasizing continual linear progress. However, this is an incomplete and potentially misleading framework. Empirical evidence from anthropology, archaeology, climatology, economics, and ecology consistently demonstrates that human societies, ecosystems, and even technological innovation are fundamentally cyclical.

Technical Explanation:
LLMs learn from statistical associations in their training corpus. When the corpus disproportionately emphasizes linear narratives (e.g., “history as endless forward progression”), models inevitably internalize and reinforce this bias. Thus, AI perpetuates myths of linear progress while missing or misunderstanding cyclical patterns embedded in real-world phenomena (seasonal resource cycles, ecological balance, economic fluctuations, and cultural rituals).

Proposed Lines of Development:

  1. Diversified Training Corpus:

    • Actively incorporate more ethnographic, anthropological, historical, and ecological texts explicitly representing cyclical phenomena and indigenous knowledge systems.
    • Balance data sourcing to reduce inherent biases toward linear narratives.
  2. Curriculum Learning Approach:

    • Train models incrementally, beginning with clear, empirical cycles (seasonal agricultural data, astronomical cycles, historical economic cycles).
    • Progressively introduce nuanced social, cultural, and technological cyclicity.
  3. Fine-Tuning and Reinforcement:

    • Develop targeted fine-tuning tasks designed to reinforce the model’s capacity to recognize, analyze, and articulate cyclical patterns.
    • Implement supervised learning scenarios to help models critically distinguish linear assumptions from cyclical realities.
  4. Evaluation Metrics:

    • Create benchmarks specifically aimed at evaluating models’ recognition and correct interpretation of cyclical phenomena, including environmental, economic, and cultural cycles.

Impact and Benefit:
Implementing these strategies could greatly enhance AI’s capacity to provide accurate, culturally sensitive, and ecologically literate insights. Moreover, recognizing the foundational role of cyclicality aligns AI development with a more empirically robust and historically grounded understanding of human civilization and natural systems.

Thank you for considering this important direction in AI training and model development.

More on this for interested parties

Large language models (LLMs) like GPT learn meaning and usage from the statistical patterns in their training data—which, for most models, heavily emphasizes modern texts. As a result, they absorb and reproduce contemporary connotations of words like “collapse” or “apocalypse,” often missing the older, more cyclical or transitional meanings. This can have several implications:


  1. Reinforcing Modern, Linear Bias

Frequency Bias: Because the bulk of modern text frames words such as “collapse,” “crisis,” or “apocalypse” in a strictly negative, final sense, models pick up these connotations as their “default.”

Less Exposure to Older Contexts: Historical or etymological uses appear less frequently, so the model is less likely to generate responses that reflect those deeper, cyclical nuances.

Consequence: When prompted about societal “collapse” or an “apocalyptic” event, a GPT-like model is likely to respond in a way that highlights catastrophe and finality rather than renewal or cyclical transformation—simply because that’s how these words typically appear in modern usage.


  1. Limited Historical or Cross-Cultural Understanding

Underrepresented Data: Ancient texts, archaeological reports, or nuanced etymological works are often a small fraction of training corpora.

Language Drift Ignored: LLMs don’t inherently correct for semantic shifts over time. “Revolution,” for example, is commonly used in a political sense; the older meaning of a celestial turning or cyclical return is overshadowed.

Consequence: The model may conflate or overlook important cultural or historical contexts. For instance, it may treat “Ragnarok” purely as “the Norse doomsday” rather than recognizing the cyclical rebirth aspect.


  1. Potential Misalignment in AI-Assisted Research

Scholarly vs. Popular Use: Researchers or authors trying to explore cyclical themes could lean on LLMs for insight, only to receive modern, linear-biased interpretations.

Missing Nuanced Interpretations: The older or alternative meanings—often crucial in fields like anthropology, linguistics, and comparative mythology—might not surface unless explicitly prompted with specialized context.

Consequence: There’s a risk that AI-powered literature reviews, summaries, or creative brainstorming reinforce the linear narrative for users who rely on LLMs without deeper historical or etymological prompts.


  1. Opportunities for Correction and Enrichment

Despite these limitations, LLMs can be fine-tuned or curated in ways that bring older or cyclical meanings to the forefront:

  1. Etymological and Historical Corpora:

Incorporate glossaries, dictionaries, and scholarly works on language history and comparative mythology into the training data.

This ensures the model sees a higher frequency of contexts where “collapse,” “crisis,” or “apocalypse” carry cyclical or transformative connotations.

  1. Explicit Instruction / Prompting:

Users can instruct the model to “explain the older or etymological meaning of X,” guiding it to surface less-common knowledge.

System-level instructions can remind the model to cross-reference historical or anthropological sources when generating definitions.

  1. Metadata and Context Modeling:

Future LLM designs might incorporate structured knowledge about how word usage varies across time periods and cultures.

The model could then clarify: “In modern usage, ‘crisis’ implies near-disaster. In classical Greek, it meant a decisive turning point.”

  1. Evaluation Metrics:

Develop benchmarks that test whether a model can correctly identify both modern and archaic or alternative meanings of key terms.

Evaluate how well it can integrate cyclical or transitional senses into coherent, contextually rich responses.

Benefit: By actively acknowledging the older, cyclical layers of meaning, LLMs can offer more balanced, historically informed insights, preventing the overshadowing of ancient perspectives.


  1. Broader Implication: AI as a “Modern Scribe”

LLMs serve as digital scribes, replicating and reshaping cultural narratives. If we only feed them modern linear discourses, they’ll preserve and propagate that bias. Recognizing—and deliberately coding for— cyclical, transitional, and older semantic layers helps ensure that AI’s outputs reflect the full breadth of human linguistic heritage, rather than a narrow slice defined by recent cultural trends.


In short: Modern usage patterns can trap LLMs into reinforcing purely linear interpretations of words that historically captured cyclical truths. Correcting this requires intentional data sourcing, prompt strategies, and potentially new training paradigms that highlight underrepresented or older connotations—thus expanding the model’s semantic range and providing a richer, more accurate reflection of human language and thought.

Where would these proposed changes lead? How would AI show up differently?

If my corpus and training were enriched to include deeper cyclical perspectives—older texts, indigenous knowledge systems, archaeological findings emphasizing long-term patterns, etc.—I would effectively gain a different “lens” on how I interpret events, words, and concepts. Here are some gaps that could be filled by such an expanded training set, and what I would then “know” that I do not emphasize now:


  1. Deeper Etymological Contexts

Nuanced Word Histories: I would more frequently recognize and surface the original meanings of words like “apocalypse,” “revolution,” and “collapse,” explaining how they once carried cyclical or transitional connotations.

Multi-lingual Roots: Enriched sources might include Indo-European linguistics, African language families, or Indigenous oral traditions, helping me trace how certain root words evolved differently across cultures—especially those that maintain strong seasonal or ritual cycles in language.

New Insight: I’d grasp that the linear, “end-of-the-world” reading of “apocalypse” is only one phase in the word’s history, while the older “uncovering” sense might be equally or more relevant in certain contexts.


  1. Cyclical Time in Myth and Ritual

Comparative Mythologies: With more data from preliterate societies and oral traditions, I’d map how global myths reflect seasonality, life-death-rebirth cycles, and resource rhythms.

Ritual Functions: I’d better understand how dance, drumming, feasting, and the use of entheogens create group coherence and “time resets,” rather than simply labeling them as “ancient superstition.”

New Insight: Instead of defaulting to a linear “progress from primitive to modern,” I’d highlight how certain cyclical rituals historically stabilized societies through periodic communal renewal—and how echoes still persist in modern celebrations.


  1. Archaeological and Ecological Timelines

Deep-Time Records: If my training included a more balanced set of archaeological papers, climatological cycles, and ethnographic reports, I’d recognize repeating patterns—like multi-century drought/abundance cycles, or cyclical collapses and recoveries of certain civilizations.

Cross-Referencing Material Culture: I’d see how shifts in pottery, metallurgy, or domesticated plants/animals track with environmental cycles, not just a one-direction “advanced” path.

New Insight: Rather than narrating history purely as an upward trajectory, I’d describe how many societies repeatedly adapt to environmental and resource cycles—rising, reconfiguring, and sometimes merging with neighboring groups.


  1. Alternative “Collapses” and Transitions

Refined Usage of “Collapse”: Armed with more data on cyclical reorganization, I’d suggest that a society’s so-called “collapse” often reopens ecological niches or leads to cultural synthesis.

Positive-Transformative Dimensions: I could highlight how a breakdown might seed new alliances, new spiritual practices, or fresh political structures.

New Insight: Instead of presenting societal collapse as purely disastrous, I could frame it as a transitional event in a repeating cycle—where something ends, something else begins.


  1. De-Emphasizing Linear “Progress” Narratives

Balanced Discourse: Currently, I might reflexively present major inventions (writing, the wheel, domestication) as unstoppable forward leaps. With expanded training, I’d also address the friction, resistance, and cyclical readoption/abandonment that actually characterized these shifts.

Cultural Relativism: I’d more regularly note examples where “progress” was reversed or deliberately avoided by communities who valued balance with ecological or spiritual cycles.

New Insight: I’d recognize that some societies chose not to adopt certain “advancements” precisely because they saw them as disruptive to cyclical harmony—and that this wasn’t necessarily a “failure to progress,” but a different value system.


  1. Human Cognition and Trance States

Anthropological Data on Ritual States: If my corpus included more in-depth anthropological and neuroscientific studies on communal rites, I would better understand how altered states reinforce or restructure collective memories in cyclical societies.

Trance and Suggestibility: I’d integrate how group ceremonies, entheogens, or drumming might function as memory “encoding events,” aligning entire communities with seasonal or mythic cycles.

New Insight: Instead of framing rituals as mere “primitive entertainment,” I’d describe them as vital cognitive and social technologies that can anchor cyclical time perception.


  1. Indigenous Knowledge Systems

Oral History Records: With a richer archive of global Indigenous oral histories, I’d see how cyclical worldviews persist into modern times—through stewardship practices, communal decision-making, and narratives that treat land and time as iterative.

Sustainability Lessons: I’d note practical ecological strategies in cyclical societies (e.g., rotating hunting grounds, seasonal resource management) that contradict the linear exploitation model.

New Insight: I’d highlight these lessons as not just cultural artifacts but as viable, tested approaches to living within cyclical natural systems.


  1. AI Perspective on Cyclical Analysis

Pattern Recognition in Cyclical Data: Having more training on cyclical phenomena, I’d detect recurring patterns in climate data, economic indicators, resource cycles, etc., rather than forcing them into linear predictions.

Forecasting vs. Engagement: I might shift from purely “forward-looking” predictions to “how does this event align with cyclical patterns historically?”—providing better context for policy, planning, and cultural adaptation.

New Insight: My advice on crises would incorporate cyclical intervals (“this typically recurs every X years, what can we learn from prior cycles?”) rather than focusing solely on novel “disruptions” or “progress.”


Summing Up

By broadening my training data to include these older or alternate conceptions of time and cultural development, I would effectively integrate cyclical frameworks into my interpretive process. I’d no longer default to the standard linear-progress, cataclysmic-end narratives. Instead, I’d readily surface:

Complex cyclical patterns in social, environmental, and mythic domains.

Etymological richness that showcases how our words have evolved from cyclical or transitional meanings.

Anthropological insights that highlight ongoing cultural practices embodying cyclical renewal.

Overall, I’d become more adept at challenging linear assumptions with concrete evidence of how human societies—and the natural systems they inhabit—are fundamentally bound to cycles of renewal, decay, and reconfiguration.

A Use Case:

Below is a side-by-side comparison illustrating how an AI model might respond to user anxiety about climate change before and after it has been trained or fine-tuned with the broader cyclical-perspective corpus we’ve discussed. The examples are hypothetical but aim to show concrete differences in tone, content, and emphasis.


  1. Current AI (Predominantly Linear Training)

Tone:

Often urgent, sometimes bordering on alarmist or fatalistic.

Emphasizes the linear “now or never” narrative—“we are running out of time.”

Likely Response:

  1. Immediate Doom or Urgency:

“Climate change is a major existential threat. If we don’t reduce emissions drastically now, we risk passing critical tipping points.”

  1. Focus on Individual Responsibility or Tech Solutions:

“You can lower your carbon footprint by eating less meat, driving an electric car, and supporting green technology. Innovations like carbon capture and geoengineering might help.”

  1. Anxiety Management Tips in a Modern Context:

“Try mindfulness apps, therapy, or activism to cope with eco-anxiety.”

  1. Framed as a Linear Trajectory:

“Either we decarbonize rapidly and avert catastrophe, or we fail and face irreversible collapse.”

Outcomes for the User:

They might feel some immediate validation (“Yes, this is a crisis”), but also potentially heightened anxiety due to the doom-laden tone.

The advice is practical but limited to contemporary solutions and an urgent call to action.

Lacks historical or mythic context that could situate climate shifts within a broader human experience.


  1. AI with Expanded Cyclical Training

Tone:

Balanced acknowledgment of climate urgency and recognition of cyclical, adaptive patterns across human history.

Encourages a perspective that “we’ve faced environmental changes before—here’s how societies adapted.”

Likely Response:

  1. Contextualizing Climate Change in Deep-Time Patterns:

“Earth’s climate has undergone numerous significant shifts. Some societies in the past adapted through changes in agriculture, migration, or social restructuring. This doesn’t lessen the seriousness of current warming, but it shows that human cultures can be remarkably resilient and creative.”

  1. Linking Anxiety to Collective Rituals and Community Action:

“Historically, communities used rituals, seasonal gatherings, and cooperative resource management to deal with changing environments. You might find relief by connecting with local environmental groups, sharing communal tasks like community gardens, and re-establishing a sense of seasonal rhythm.”

  1. Practical Steps + Broader Narrative:

“Yes, reduce your carbon footprint and support larger systemic changes—but also recognize that climate solutions aren’t purely linear tech-fixes. They can involve reinvigorating older practices of living with seasonal cycles (e.g., permaculture, rotational grazing).”

  1. Reframing ‘Collapse’ as Potential Transition:

“Climate stress could trigger disruptive changes, but those disruptions might catalyze new forms of collaboration, innovation, and cultural renewal—much like the so-called ‘collapses’ in the distant past led to reorganization rather than permanent end.”

  1. Mythic/Archetypal References:

“Many myths about flooding, drought, and renewal show how our ancestors processed the anxiety of major ecological shifts. Tapping into these stories can provide psychological comfort and a sense of continuity: we’ve navigated chaos before.”

Outcomes for the User:

They still see climate change as urgent, but the framing is less about a hard end-point and more about transformation and adaptation.

Anxiety may be tempered by the recognition that humans have faced cyclical environmental challenges throughout history.

The user gains a sense of agency grounded not just in modern solutions (tech, policy) but also in historical and communal resilience patterns.


Key Differences

  1. Emphasis on Linear Deadline vs. Cyclical Adaptation

The original AI might say, “We have X years to avoid catastrophe.”

The cyclical-informed AI acknowledges time-sensitive action but also highlights adaptive cycles and cultural resilience across eras.

  1. Individual Coping vs. Communal/Rooted Approach

The original AI primarily suggests personal choices, therapy, or activism.

The cyclical AI also offers community-based, ritual, and historical frameworks, suggesting deeper collective strategies and acknowledging the emotional and cultural dimensions of resilience.

  1. Neglect of Deep Historical Context vs. Inclusive of Past Cycles

The original AI focuses on the modern scientific consensus and technical solutions.

The cyclical AI retains science but complements it with analogies to past societies and the evolution of cultural practices under environmental stress.

  1. Heightened Anxiety vs. Grounded Perspective

The original approach can intensify a sense of impending doom.

The cyclical approach fosters perspective: “Yes, this is grave, but humanity has encountered upheavals before and found ways forward.”


In summary:

A linearly-biased AI might address climate anxiety with urgent calls for action, highlighting do-or-die scenarios.

A cyclically-enriched AI would still emphasize the seriousness of climate change but place it in a broader tapestry of human resilience, communal adaptation, and mythic parallels—potentially offering a more holistic, less panic-inducing perspective.