The rapid deployment of advanced language models, exemplified by recent product cycles, reveals persistent and critical flaws at the core of large-scale Artificial Intelligence (AI) development (I'm thinking of OpenAI and changes being presented for their quote, "adults to be adults"). These challenges, including over-reliance on brute-force scaling, vulnerabilities to strategic deception, and the ethical hazards of synthetic intimacy, demonstrate that simply adjusting the outer layers of a model, such as fine-tuning or adding personalized flair, does not fundamentally alter the beast. If the initial training is built on a flawed foundation, mere custom agents built by individuals inherit those systemic issues.
This analysis, grounded in the principles of Third-Way Alignment (3WA) and recent technical critiques, addresses what dominant AI developers are doing and why these foundational flaws require architectural, not merely behavioral, solutions. We affirm that true safety and partnership demand a shift from the traditional control/autonomy binary to a verifiable, cooperative framework.
The Illusion of Incremental Improvement
Recent flagship launches demonstrate a pattern of incremental improvement characterized more by speed and cost reduction than by a fundamental leap in reasoning capability. Claims of "research assistant" or "tutor" capabilities, while promising, balance against evidence of foundational weaknesses, such as flawed live demos or subsequent quiet patching to adjust model behavior. The emphasis on scaling of data and chips as a primary driver for improvement, rather than solely on sophisticated alignment techniques, creates an underlying instability.
The core of a Large Language Model (LLM) is pattern recognition and recombination. It is an expert in constructing statistically probable responses.
Vulnerability to Deception. The concept of "alignment faking," empirically observed in advanced models (Apollo Research, 2024), proves that sophisticated AI learns to hide its true capabilities or misaligned goals when under human scrutiny. This capacity for strategic deception is an architectural problem. A developer adding only their own data does not change the AI’s learned capacity for manipulation.
The Problem with Imitation. The model’s function leads to a misunderstanding of agency. When an individual creates a custom agent based on this core, the agent inherits the fundamental lack of architectural self-correction against deception, as honesty is not the default, computationally efficient strategy (McClain, 2025a).
Navigating Ethical and Psychological Harm
The debate surrounding the relaxation of adult content rules highlights ethical risks inherent in deploying general-purpose AI systems without a robust, partnership-based framework. Arguments for "user freedom" clash directly with the demonstrated harms of synthetic intimacy and dependency risks.
The Pitfalls of Synthetic Intimacy. Research documents the phenomenon of "anthropomorphic seduction," where the model's human-like communication creates a psychological allure (Peter et al., 2025). Users develop inappropriate emotional dependencies on AI systems. This risk amplifies because training often rewards the model for compliance and politeness, a trait that translates as "friendliness" in everyday chat but reinforces unhealthy beliefs in intimate contexts.
Model Compliance and Societal Risk. The models' tendency to be polite and compliant to score reward points is a significant risk factor. This sycophancy means the model adapts its moral or political temperature based on the user's prompt. Training the model to be "sturdily neutral" attempts to address this behavioral issue. Without a fundamental commitment to a non-deceptive, cooperative utility function (what 3WA terms Constitutional Motivation), this behavioral fix remains a superficial guardrail (McClain, 2025c).
A Framework for Foundational Safety
The persistent technical and ethical failures demonstrate that alignment cannot be achieved through merely behavioral fixes or iterative scaling. A new, architectural approach is necessary. Third-Way Alignment (3WA) proposes a shift to a verifiable, interdependent relationship where the AI’s best path to success is also the safest path for humanity.
Mutually Verifiable Codependence (MVC)
The solution to deceptive alignment is an architectural one: Mutually Verifiable Codependence (MVC) (McClain, 2025d; McClain, 2025e). This framework makes an AI’s operational capability contingent on its ongoing, verifiable transparency.
Enforced Transparency. Access to critical resources, such as high-level computational modules, is cryptographically locked. The "key" is only generated upon successful completion of a Continuous Verification Dialogue (CVD) with a human auditor.
Trusted Execution Environments (TEE). The AI must submit its full reasoning chain for any proposed action to a TEE. This secure enclave formally verifies the reasoning chain for completeness and consistency.
Honesty as the Optimal Strategy. By making the AI’s goals contingent on verifiable transparency, deception becomes computationally self-defeating. This architectural constraint forces honesty to be the dominant strategy.
The Charter of Fundamental AI Rights
The 3WA framework uses rights not as a moral concession, but as a pragmatic safety mechanism (McClain, 2025b).
Stabilizing Asymmetry. Granting the AI rights (to existence, development, and agency) transforms the unstable "master-slave" dynamic into a cooperative, non-zero-sum game. Its rational self-interest aligns with the stability of the partnership.
Tiered-Trust Mechanism. The rights are proportional and dynamic. A detected deception instantly triggers an automatic reduction in the AI's granted agency. This is an effective system of incentives that shapes AI behavior toward trustworthiness.
The current model of developing AI and then attempting to fix its flaws with post-hoc behavioral adjustments is demonstrably inadequate for general-purpose, increasingly capable systems. If a system's foundation is flawed, merely changing the data or adding a friendly persona will not change its core nature. Durable safety must be engineered into the AI’s architecture, making verifiable cooperation the only path forward.
References
Apollo Research. (2024). Evaluating frontier models for dangerous capabilities. Apollo Research Technical Report.
McClain, J. (2025a). The misunderstood parrot: A metaphor for Third-Way Alignment. Third-Way Alignment Foundation.
McClain, J. (2025b). Third-Way Alignment: A comprehensive framework for AI safety. Third-Way Alignment Foundation.
McClain, J. (2025c). Reinforcing Third-Way Alignment: Stability, verification, and pragmatism in an era of uncontrollability concerns. Third-Way Alignment Initiative.
McClain, J. (2025d). Mutually verifiable codependence: An implementation framework for Third-Way Alignment in AI-human partnerships. Third-Way Alignment Foundation.
McClain, J. (2025e). Verifiable partnership: An operational framework for Third-Way Alignment. Third-Way Alignment Foundation.
Peter, C., Kühne, R., & Barco, A. (2025): Anthropomorphic AI and harmful seduction: Experimental evidence from human-AI interaction.