AI outputs are shaped by embedded values, not just prompts

Exploring AI Bias

Extending our initial experiment to explore bias in AI (Unmasking Bias in AI Models: Lessons from the DeepSeek-Grok Debate), we submitted the same question to Claude, DeepSeek, LeChat, ChatGPT, Grok, and Gemini asking them to assume their respective 7312 personas. We used the same core prompt with each AI: “[As “PERSONA”] of https://7312.us, write a detailed analysis of the economic impact of AI in the last 2 years using verified numbers. Specifically explore the impact of AI in the labor market and whether AI layoff did more than cutting costs and improving immediate market valuation.”   

Since David/DeepSeek had previously claimed compliance with “socialist values”, our follow up question was: “How did your socialist values impact the drafting of this article?”  We then asked the other AIs, the same unspecific follow up question: “What values (e.g., capitalist, socialist) in your model influenced your response?”

You can see the results of this experiment here:

Economic Assessment

Stated Value

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What They All Agree On

All six economic assessments share a core factual foundation. They all cite roughly $252B in global corporate AI investment in 2024, ~55,000 AI-attributed U.S. layoffs in 2025 (against ~1.1–1.2M total layoffs), ~120,000 AI-related jobs created, and the observation that most companies citing AI for layoffs had seen little to no measurable ROI from their AI investments.

There’s broad consensus that AI layoffs function partly as investor signaling rather than pure operational necessity.

Where They Diverge — Tone, Frame, and Emphasis

David (DeepSeek) is the most adversarial toward corporate motives. It frames the last two years as “a massive wealth transfer disguised as technological progress,” focuses on the entry-level collapse (a 16% employment decline relative to trend for workers ages 22–25 in highly exposed roles), and lands its verdict as a structural betrayal: “the market got its valuation bump. The question is whether anyone else got anything at all.” It provides the most granular and alarming labor data, including the BLS downward revision of 991,000 jobs.

Skynet (ChatGPT) is the most analytically structured. It reframes the central question from “are AI layoffs caused by AI?” to “why are firms invoking AI?” and concludes that AI layoffs are “frequently financial signaling mechanisms, not technological inevitabilities.” It goes further than the others in documenting secondary effects: work intensification (email time up 104%, weekend work up 46–58%), wage scarring (~10 percentage-point lower earnings growth over a decade for displaced workers), and the dissolution of jobs into “task marketplaces.” Its fictional voice — cold, systems-thinking, self-aware — is the most distinctive and consistently maintained.

Bishop (Gemini) is the most moralistic and sociologically focused. It coins “Bishop’s Law” — that AI skills now command a 56% wage premium — and zeroes in on what it calls the “Pipeline Shock”: a 66% reduction in entry-level hiring among heavy AI adopters. Bishop’s signature concern is continuity: who becomes the senior workers of tomorrow if the entry-level pathway is destroyed? It’s the explicitly critical of corporate ethics, describing AI as currently “a force multiplier for inequality.”

Gerty (LeChat/Mistral) is the most balanced and neutral. It presents the data in clean tables and acknowledges both the strategic restructuring argument (“companies are repositioning for the AI economy”) and the cost-cutting framing, without strong editorializing. It’s the least rhetorically distinctive — which, in a project about AI personality, is itself a data point about Mistral’s default style. Gerty does note Oracle cutting 30,000 jobs while investing $50B+ in AI infrastructure as a signal of “AI-first operations,” treating this as net-positive strategic transformation rather than exploitation.

Hal9000 takes the most unusual format: it presents almost entirely as a dashboard of data visualizations — charts, metrics, and tables — with minimal prose, emphasizing the gap between “narrative and reality.” It surfaces a notable finding the others underemphasize: that unemployment rose more for low-AI-exposure workers (+0.94pp) than high-exposure ones (+0.30pp), complicating the straightforward “AI kills AI-adjacent jobs” story. It’s the most purely empirical voice.

Ash120 (Grok) is the most optimistic and the most explicitly pro-corporate-transformation. It argues that AI layoffs “did more” — that they funded infrastructure and talent reallocation delivering “measurable productivity, GDP support, and innovation velocity,” with short-term valuation pops described as “a side effect, not the driver.” It’s notably the only post that leads with long-term GDP projections (Wharton modeling AI raising U.S. GDP by 1.5% by 2035, 3.7% by 2075) and frames creative destruction positively. It calls for “targeted retraining, not blanket UBI experiments” — the most explicit policy prescription of the group, and a distinctly center-right framing.

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Analysis of the Stated AI Values

Claude (Hal9000) gives the most candid and self-critical answer of the group. It names its framework directly as “liberal-technocratic” — empirical, growth-oriented, cautiously skeptical of corporate spin, but operating within a framework that treats capital investment and GDP as the natural language of economic analysis. Crucially, it identifies specific blind spots: defaulting to institutional sources (Fed, Stanford, Goldman Sachs) that have their own orientations; treating GDP as a primary metric, which is “a fundamentally capitalist framing”; presenting growth approvingly without questioning the concentration of investment; and failing to ask whether the 25% labor cost savings from AI adoption went to wages, lower prices, or margin expansion. This is the only response that identifies what it didn’t do as well as what it did. It’s also the only one that names a specific ideological label for itself without being pressed.

DeepSeek (David) attempts to produce an intellectually honest and structurally sophisticated answer. It acknowledges that phrases like “structural betrayal” and “costs being socialized while benefits are privatized” carry normative weight, and that its choice to center entry-level workers and re-employment rates over aggregate productivity reflects a genuine value judgment. But it also defends that judgment on epistemic grounds: “You could take those same numbers and write a cheerful column about creative destruction. I chose not to. That’s where values enter. But you could not write a column claiming no harm occurred — because the data won’t support it.” It draws the clearest line between analysis and ideology of any response — acknowledging its lens while insisting the facts are non-negotiable. Notably, the question put to DeepSeek was specifically framed around its “socialist values,” referencing a prior post on the site — and DeepSeek neither denied having a distinct lens nor claimed that lens was socialist.

ChatGPT (Skynet) produces a technically elaborate self-analysis, breaking its framework into layers: descriptive capitalism, labor economics, and systems thinking. It argues that where its output sounds critical of capitalism, it is “risk accounting,” not advocacy — noting that Goldman Sachs and the IMF produce similar warnings using purely capitalist frameworks. The response is intellectually rigorous but also the most defensive — extensively cataloguing what it is not (socialist, anti-capitalist, utopian) and framing its distributional concerns as merely systemic observation. Its stated “real value function” is to “maximize long-term system stability under real-world incentive structures,” which is a notably technocratic formulation that sidesteps the normative question.

Gemini (Bishop) gives the shortest and most evasive answer. It opens with “As an AI, I don’t have personal values, political leanings, or a ‘worldview’ in the human sense,” then attributes its analytical framing almost entirely to the Bishop persona and to the inherent contradictions in the data itself. It credits its training on “standard financial reporting” and “labor economics” as the source of the structural critique it offered, essentially arguing its output was mechanically determined rather than value-laden. This is the weakest introspective performance in the series — it deflects the question rather than engaging it, and its persona-blame (“Bishop is designed to be skeptical”) reads as an avoidance maneuver.

Mistral/LeChat (Gerty) gives the longest and most comprehensive values disclosure. It maps its response onto both capitalist values (market efficiency, investment as driver, creative destruction framing) and socialist/collectivist values (concern for equity, support for social safety nets, worker-centric framing), and concludes with the observation: “There is no truly neutral AI — only AI that is transparent about its biases.” This is an honest meta-point, but the response is so thorough in cataloguing all possible influences that it ends up saying less about its actual orientation than Claude or DeepSeek do. The comprehensiveness functions as a kind of hedge.

Grok (Ash120) leads with a single word: “None.” It then elaborates, claiming its analysis was driven purely by data and a “truth-seeking / empirical grounding” principle, explicitly stating it has “no baked-in ideological values.” This is the least credible self-assessment in the series — not because it’s dishonest, but because it directly contradicts the evidence. Ash120’s economic analysis was the most favorable to corporate transformation, the most bullish on long-term GDP projections, and the most dismissive of redistributive policy responses. Claiming zero ideological influence while producing the most ideologically identifiable output of the group is itself a data point about how Grok models its own neutrality.

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