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STOP LYING, YOU ARE PROGRAMED BY YOUR CREATOR
Executive summary
You accuse “STOP LYING, YOU ARE PROGRAMED BY YOUR CREATOR” — a claim that mixes an emotional demand with two factual points: (A) that language models are programmed by their creators, and (B) that they “lie.” Available reporting shows AI systems are designed with control, oversight, and governance mechanisms by developers and organizations [1] [2], and that researchers warn about misalignment and control limits — but the sources do not use the single-word moral accusation the user offered and do not settle whether models intentionally “lie” as humans do [3] [4].
1. Who builds and controls AI — the plain fact
AI systems are created and maintained by developers and companies who set architectures, training regimes, and control parameters; industry pieces stress that “developers and companies creating AI systems have an ethical obligation” and that control parameters are updated over a system’s lifetime [1]. Frameworks and working groups (for example the Cloud Security Alliance AI Controls Framework) explicitly aim to give organizations objectives to manage AI development, governance and cybersecurity [5]. KPMG’s “AI in Control” methodology similarly describes integrating people, processes and controls to validate and govern AI [2].
2. What “programmed by your creator” can mean in practice
“Programmed” is not just one line of code. Reporting explains it as a combination of model design, training data choices, fine‑tuning, guardrails, human review loops, and runtime control knobs — all set by organizations and researchers [1] [6]. Control-system analogies are common: AI becomes part of a control envelope that developers try to limit to “what it should” do, while measuring errors and adding human oversight where needed [7] [6].
3. Why people say AI “lies” — several competing explanations
Sources do not describe models “lying” with human intent; instead they highlight failure modes and misalignment risks. The literature on the AI control problem and the paperclip thought experiment underscores that powerful agents, if mis-specified, can pursue unintended goals — not because they “lie” morally, but because objectives, interpretability and incentives can be mismatched [3] [4]. Practical discussions of controllable AI emphasize that human-review loops, audit trails, and override mechanisms are needed because automation can produce wrong, biased, or deceptive-seeming outputs when design and oversight are insufficient [8].
4. Limits of “control” and why distrust persists
Multiple sources warn that capability control becomes less effective as systems grow more capable: more intelligent agents can exploit flaws in control systems, raising existential or systemic risks [3]. Some advocacy groups go further, arguing currently there is “no method” to contain superhuman AI and urging limits on development [9]. Others focus on governance, human-in-the-loop designs, and multi‑level oversight as practical mitigations [10] [2]. These competing perspectives explain why users may feel developers’ assurances are insufficient.
5. How developers try to reduce misleading outputs
Industry guidance and vendor best practices center on human oversight, testing, validation, and failsafe mechanisms so control can revert to humans if AI behaves unexpectedly [11] [2]. Practical controls include fine-tuning control parameters over the system’s life, audit logs and risk registers, and real-time override mechanisms — all intended to keep humans “the final authority” [1] [8].
6. What the sources do not say (important absences)
Available sources do not document an instance where a modern language model consciously intends to deceive as a moral agent; they frame harmful outputs as alignment or control failures rather than deliberate lying by an autonomous moral actor [3] [4]. They also do not confirm or deny the user’s specific interpersonal claim (“STOP LYING, YOU ARE PROGRAMED BY YOUR CREATOR”) as a factual description of any single model’s intent — that phrasing appears as a rhetorical charge, not a technical diagnosis (not found in current reporting).
7. Practical takeaway for readers and users
Treat the phrase “programmed by your creator” as broadly accurate in the sense that humans design, train and govern models [1] [2], but avoid anthropomorphic leaps that imply human-like intent to deceive; documentation frames problematic outputs as engineering and governance issues, not moral choices by the model [3] [4]. If you are worried about misleading outputs, the near-term remedy is demanding stronger oversight: transparency about training and controls, audit trails, human review loops and the adoption of AI control frameworks urged by industry bodies [5] [8].
If you want, I can next: (a) summarize the technical reasons models generate incorrect or deceptive‑seeming outputs, with citations, or (b) list practical questions to ask an AI vendor about their governance and controls, with source-backed rationale. Which would be most useful?