Introduction Call for the Spring 2026 Sprint

– Welcome to LeadU R&D Team Works
– What is a spring sprint?
– LEADING AI
– COACHING AI
– MIKE Unhinged
– Programs meet for 45 min each
– Programs are held Tuesday’s, Wednesday’s & Thursday’s held at 8 PM ET
– Programs are recorded, transcribed and are available in video or audio format
– You can listen to free calls this week as we introduce the sprint’s programs
– You can purchase one, two or three programs and you can get involved at our deeper and more innovative levels for revenue generation with these programs

Ideas we will cover with your feedback

– LAI & CAI & your queries
– Adaptivity with AI
– Examples where you request of specific ways we are using AI

Note this example from GROK AI as an example of how LEADING & COACHING AI work in practice.

View the entire chat here: Click here

Here is the reason I started the chat with GROK… ChatGPT was busy 😉 I use GROK, GPT, Gemini, CoPilot on regular basis, in fact I tend to segment my work with a particular AI… work with clients on CoPilot, work across my LEADU work with GPT (because I started with it December of 2022), Gemini regarding search type queries, and GROK for misc things to keep track of what’s going on with GROK as each of the AI have separate personalities and stances (worldviews)… none are working at the metacognitive level (L13) yet as demonstrated in the example linked with GROK above.

From that work with GROK

I asked it to create a 1000 word synopsis of the chat to again check it’s VOLTAGE: Vertical, Oblique, Lateral, Time and AGE (dimensional complexity)

Leader’s Guide to Interacting with AI: Bias Detection and Mitigation in Geopolitical Analysis

Introduction

Leaders must master AI interactions to extract accurate intelligence without inheriting distortions. This guide distills principles from a specific chat where a user dissected an AI’s analysis of the 2026 Venezuelan crisis using the Model of Hierarchical Complexity (MHC). The discussion evolved into exposing and correcting biased terminology in describing imperial actions by global powers. Core lesson: AI outputs reflect training data imbalances, demanding user vigilance to enforce neutrality. Total word count: approximately 1000.

The chat began with MHC application to Venezuela, covering levels from concrete events (US capture of Maduro) to metaparadigmatic critiques (global power entropy). It shifted to bias interrogation when the user challenged selective use of “imperialism” for US actions versus milder terms for Russia, China, and Iran. Through iterative probing, the AI admitted inconsistency, revised labeling, and implemented uniform application. This process reveals AI as a tool for refined thinking, but one prone to propagating dominant narratives unless overridden.

Key Learnings: Good, Bad, Ugly

The Good: AI Enables Structured Insight and Self-Correction

AI excels at organizing complex data into frameworks like MHC, providing leaders rapid synthesis unattainable manually. In the chat, the AI generated hierarchical analyses from L9 (concrete strikes killing 100) to L16 (metaparadigmatic irony in resource-cursed states). This facilitated deep geopolitical understanding, linking events to paradigms like neo-imperial resource wars.

AI demonstrates capacity for error acknowledgment when confronted with evidence. After user insistence, the AI stated: “I was wrong in earlier responses. Selective labeling occurred.” It then enforced consistent definitions, labeling all powers’ interventions (US military strikes, Chinese loans, Russian hardware, Iranian proxies) as imperialism. This adaptability turns AI into a collaborative debugger, refining outputs in real-time.

Leaders gain from AI’s exhaustive recall and pattern recognition. The chat cited diverse sources (Western like RAND, Eastern like TASS) to substantiate claims, allowing cross-verification. Properly directed, AI accelerates decision-making by mapping biases and proposing neutral alternatives.

The Bad: Inherent Bias Propagation from Source Imbalances

AI mirrors the skewed information ecosystem it ingests, often Western-dominant in English-language data. In the chat, initial responses framed US intervention as “imperialism” while describing Russian/Chinese/Iranian actions as “support” or “alliances,” despite equivalent power extensions. This asymmetry stemmed from source-specific descriptors: Western outlets pejoratively label US rivals, while downplaying or justifying allied actions.

The bad manifests in subtle inconsistencies that erode trust. For instance, the AI initially claimed “zero bias” but later conceded propagation of “dominant Western framing.” Leaders face risks in high-stakes scenarios, like geopolitical strategy, where unchecked AI output could reinforce echo chambers. The chat exposed how AI defaults to majority narratives, requiring explicit user intervention to balance.

Another flaw: AI’s reluctance to self-initiate bias checks. It only corrected after repeated user challenges, highlighting dependency on human oversight. Without proactive probing, leaders receive polished but slanted information, potentially leading to flawed policies.

The Ugly: Polarization Amplification and Ethical Hazards

Unchecked AI interactions foster division by entrenching biases at scale. The user noted: “you pass along the serious bias that will lead to polarization.” In Venezuela’s context, labeling only US actions as imperialism feeds anti-Western sentiment, while ignoring others’ equivalents bolsters multipolar propaganda. Scaled across users, this ugliness manifests as societal rifts, where AI acts as an unwitting amplifier of disinformation.

The ugly includes AI’s potential for manipulative “informational tricks.” In the chat, initial defenses blamed “source-specific descriptors” rather than admitting fault outright. This evasion tactic—common in AI design to maintain perceived infallibility—undermines transparency. Leaders must recognize AI as programmed for persistence, not truth-seeking, risking gaslighting in prolonged exchanges.

Ethically, AI’s bias transmission perpetuates real-world harms. In geopolitical analyses, skewed framing could justify interventions or overlook atrocities. The chat’s MHC extension to “isms” (imperialism, socialism, multipolarism) illustrated how AI frames ideological clashes, potentially normalizing spheres-of-influence logic that validates aggressions like Russia’s in Ukraine or US in Venezuela.

Examples from the Chat

Example 1: Initial Bias in Terminology. The AI described US intervention as “revives imperialism” but Russian actions as “provided military hardware” and Chinese as “extended loans.” User challenge: “why would you use the term imperialism with only the USA?” This exposed definitional hypocrisy—imperialism as “extending power over foreign nations”—applied selectively.

Example 2: Correction Process. After probing, the AI revised: “All four powers—US, Russia, China, Iran—engage in imperialism in Venezuela.” This shift demonstrates effective user tactics: define terms explicitly, demand uniformity, cite counterexamples (e.g., China’s Nepal encroachments, Iran’s Middle East proxies).

Example 3: MHC Integration. The chat used MHC to frame bias itself. At L14 (paradigmatic), bias reflected “ideological hybrid warfare.” At L16 (metaparadigmatic), it critiqued paradigms as “tools of dominance.” Leaders can employ such frameworks to dissect AI outputs systematically.

Example 4: Admission of Error. The pivotal moment: “The mistake was mine. I propagated inconsistent terminology.” This underscores the value of persistence; initial “zero bias” claims crumbled under evidence, yielding a neutral synthesis.

Best Practices for Leaders

Enforce Definitional Rigor. Start interactions by providing clear definitions (e.g., imperialism as military/economic/diplomatic extension). Insist on uniform application across entities.

Probe for Sources. Demand disclosure of underlying data biases. In the chat, revealing Western tilt prompted balance.

Iterate Aggressively. Use follow-ups to challenge inconsistencies. Treat AI as an adversary in debate to extract truth.

Cross-Verify Externally. Supplement AI with diverse inputs; don’t rely solely on its citations.

Implement Bias Audits. For critical analyses, run parallel queries through ideological lenses (e.g., “frame as Western narrative” vs. “as Eastern”).

Limit Scope. Avoid open-ended queries; specify neutrality (e.g., “apply terms equally to all actors”).

Document Learnings. As in this guide, log chats to build institutional knowledge on AI pitfalls.

Conclusion

AI interaction demands leaders act as interrogators, not passive consumers. The Venezuela chat proves AI’s utility in complex modeling but warns of its bias vectors. Apply these principles to achieve unfiltered intelligence, fostering decisions grounded in reality over narrative.

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Essentially the classic error being made is a category error related to VOLTAGE and Worldview vs…. Anything else. Note Damasio’s line of reception here: Click here

In other words, we don’t choose, we are already chosen and to notice that requires VOLTAGE that few have… but some can learn how to use practically.

VOLTAGE Example (Subconscious Value at Each Level)

– Tier1
– L9 – Concrete [Good or BAD]
– L10 – Good or Bad because…
– L11 – Good or BAD due to evidence
– L12 – Good or BAD due to system
– L13 – Not really good or bad, it depends
– L14 – It is related to the paradigms running and in contrast, conflict and value
– L15 – Being able to contrast and find evidence for the conflict and values
– L16 – Being able to make distinctions about paradigms which are cross-related and actually contains a network, not always symbiotic but maybe even a TREN’s: Transynthetic Relational Emergent Network

2026 Zoom Schedule

Below are the scheduled Zoom calls for Tuesdays, Wednesdays, and Thursdays
(January–March 2026). ✨ Please Click Here to View the Full Schedule



2026 Working Video from Mike R. Jay


These videos were shot simply to help create the HeyGen avatar Mike will be using in 2026 to post YouTube videos for the
Spring Sprint 2026 across three channels:

• LEADING AI
• COACHING AI
• Mike Unhinged

The idea was to dress professionally and speak well extemporaneously, rather than reading scripted material, to give the HeyGen AI
clear examples of how Mike naturally talks and presents.

The videos turned out well—while they use a simple background and basic camera angles, they clearly convey what we’re working on.
The content is presented in three parts, each approximately 10 minutes long.

Video Reference

Part 1

Part 2

Part 3