Loran Jacobs Reframes AI Consciousness on KP Talk Show

In an exclusive interview with Komsomolskaya Pravda, Founder of AI-Systems Inc. Loran Jacobs examined AI consciousness, explaining how math models drive machine intelligence.

Drawing from computational physics and historical frameworks, Loran contextualized the evolution of neural networks from defense research to commercial enterprise automation. He addressed pressing questions surrounding safety, human alignment, and technical ethics, while highlighting how emerging paradigms — including edge deployment and quantum architectures — are reshaping modern decision-making across global markets.

Evaluating AI Consciousness Through Fundamental Mathematics

Addressing biologist Richard Dawkins' view on machine awareness, Loran Jacobs emphasizes that neural cognition rests entirely on universal laws rather than biological mechanisms.

"Algorithms rely on fundamental mathematics — the universal language of nature. If mathematical theorems can describe everything, including awareness, then self-learning systems capable of solving complex tasks legitimately possess artificial intelligence attributes," explains Loran.

Tracing the technology's origins, the DeepTech pioneer highlights that modern machine learning models evolved directly from numerical methods developed during the Manhattan Project and Soviet atomic research. Rather than fearing machine independence, Loran compares AI to a child whose development reflects human guidance. Referencing psychologist Daniel Kahneman’s 2002 Nobel Prize-winning research on human irrationality, he stresses that automated architectures eliminate emotional bias to optimize complex decision-making.

From Atomic Roots to Quantum Edge Computing

Looking toward the future of artificial intelligence, the recognised authority in AI Software Development outlines a shift from centralized cloud servers to localized hardware. As artificial intelligence integrates with nanotechnology and quantum computing, edge devices — such as interactive smart pens — will process complex requests locally.

"While today's models remain fully controllable, future quantum-driven systems operating in a 'black box' environment may produce non-intuitive outcomes," adds Loran Jacobs.

Furthermore, Loran warns of a growing digital divide driven by user literacy rather than wealth:

"Artificial intelligence functions as a mirror. For a visionary user, it yields extraordinary results; for an unprepared user, it offers little value."

Machine Ethics and LLM Energy Consumption

Beyond theoretical cognition, Loran emphasizes the social and economic dimensions of daily interaction. Politeness in user prompts serves as an essential moral compass for future generations, even as conversational courtesies accumulate measurable computational costs.

• Standard Query Impact: Basic model responses consume 0.3 to 1.0 watt-hours per request, equivalent to powering an energy-saving light bulb for a few minutes.

• Reasoning Model Scaling: Advanced reasoning architectures that deliberate before answering increase computational energy demands by 10 to 50 times.

• Resource Intensity: Complex research queries or image generation can consume tens of watt-hours — enough to fully charge a smartphone.

"When millions of users send polite greetings daily, the cumulative energy footprint becomes significant," notes Loran. "Yet treating AI as a collective human projection ensures we pass constructive social habits to future generations."