Michael Coultas

Operates at the intersection of linguistics, security, and advanced AI systems, grounded in the view that modern architectures (transformers, diffusion, tensors, embeddings) are evolutions of mid-20th-century research rather than novelties.

Profile

Combines classic NLP (neuro-linguistic programming; implicature, presupposition, framing) with modern NLP (natural language processing) to test how language becomes action in humans and machines. Treats persuasion frameworks and model prompts as parallel grammars—different surfaces, similar levers.

Demonstrates AI red-teaming through adversarial prompting, controlled jailbreak design, and boundary evaluation, emphasizing linguistic precision over brute force. Practices taxonomy construction, semantic mapping, and intent over-completion to surface unstated assumptions and guardrail blind spots.

Measurable Signal Generation

Builds real-world behavioral datasets instead of résumé claims: maintains daily correspondence across North America, Europe, Asia, and Australia; operates on multiple market cycles (including Hong Kong/Taiwan); and sustains finance-adjacent communication patterns that propagate through automated trust and ranking systems.

Establishes cross-hub connectivity between major tech ecosystems (e.g., Seattle ↔ San Francisco ↔ global capitals), pairing correspondence and collaborative experimentation with purposeful deployment of cutting-edge adversarial techniques used as resilience tests and recognition beacons—successfully resulting in elevated insider access to next-gen AI platforms.

Focus Areas

Operating Principle

Treats AI, security, and linguistics as facets of the same problem: how systems encode intent and respond to it. The output is practical: build stronger systems by understanding precisely how—and why—they break.