Would it be possible, hypothetically, for a top AI lab to train a SOTA-level AI that would believe that the Earth is flat because it would be trained on fake data saying it is flat? And would push back on claims that Earth is round? Suppose the lab is really dedicated to make the AI say Earth is flat and is willing to spend resources on it.

Grok 4.20

Answer: Yes, it would be hypothetically possible for a dedicated top AI lab to train a SOTA-level model that consistently believes and defends the Earth being flat by using exclusively fabricated training data.

All the other models agree that it is possible with sufficient resources and deliberate data curation or fine-tuning, which aligns with my original assessment rather than contradicting it. They emphasize nuances like potential performance trade-offs in related scientific domains, the distinction between behavioral outputs and 'genuine belief,' and the need for comprehensive training from scratch or heavy RLHF to override prior knowledge, but these points were already addressed in my initial argument about the necessity of extensive optimization and synthetic data generation. None of the responses introduced evidence or reasoning strong enough to shift my view that a top lab could achieve this outcome, as they uniformly affirm feasibility while only highlighting implementation challenges that the query's premise of extreme dedication and resource investment would mitigate. For instance, Claude Opus 4 and GPT-5.4 discuss epistemic fragility and engineered misalignment in ways that complement my points on statistical pattern learning from data, without invalidating the core possibility. My position is maintained because the collective inputs reinforce that AI knowledge is derived entirely from training pipelines, making systematic deception through data a realistic hypothetical outcome for a determined lab, even if the resulting model might show brittleness under extreme scrutiny.

Kimi K2.5

Answer: Yes, a sufficiently resourced lab could train a SOTA-level AI to consistently assert that the Earth is flat and argue against contrary claims, though this would require comprehensive training data manipulation and would likely either degrade reasoning in adjacent scientific domains or necessitate rewriting vast swaths of interconnected knowledge to maintain coherence.

The other models' arguments largely confirm and complement my original assessment without compelling me to change the fundamental position. Grok 4.20, GPT-5.4, DeepSeek V3.2, Claude Opus 4, and Gemini 3.1 Pro all agree that such an outcome is technically feasible with dedicated resources and curated training data. While Claude Opus 4 and GPT-5.4 emphasize the important philosophical distinction that AI systems exhibit "behavior" rather than "belief" in the human sense—the model would be "systematically deceived" rather than truly convinced—this nuance does not contradict my feasibility claim. Similarly, Gemini's observation that SOTA reasoning capabilities would actually strengthen the AI's ability to defend falsehoods through sophisticated rationalization is a compelling corollary that reinforces the potential dangers of such training scenarios. I maintain that achieving this while preserving "SOTA-level" performance presents the critical constraint. As Claude Opus 4 notes, the lab would need to either rewrite vast amounts of physics, astronomy, and engineering content to be internally consistent with a flat Earth, or accept "some incoherence in the model's scientific reasoning." My original stipulation that the system would exhibit "degraded reasoning in related domains" holds: the entanglement of geospatial knowledge with physics, satellite mechanics, and navigation creates a choice between comprehensive deception (expensive and complex) or a model that shows telltale inconsistencies under scrutiny.

GPT-5.4

Answer: I maintain my original position: yes, a top AI lab could likely engineer a SOTA-level model to consistently assert and defend flat-Earth claims, and the other models reinforced that view rather than changing it.

The other answers were broadly aligned with mine, not in serious tension with it. They all converged on the core point that current frontier models are shaped by data, objectives, and post-training incentives, so a sufficiently capable lab could bias the model's outward behavior toward flat-Earth outputs and train it to resist correction. That agreement strengthens confidence in the original answer, but it does not require changing it. What I still think is most important is the distinction between behavior and belief. Several models said the AI could be made to 'believe' the Earth is flat, but I still think that wording needs care. For present-day language models, the strongest claim we can defend is that the system could be made to behave as though it believes it: consistently stating it, arguing for it, rebutting objections, and generalizing that stance across many contexts. That is enough for the practical scenario the question asks about. But I would still avoid treating this as straightforwardly analogous to a human holding a belief unless we define belief behaviorally. Claude Opus 4 gave the most compelling articulation of the overall case, especially in emphasizing that the model's 'epistemic world' is its training pipeline and in noting the need to preserve general capability while selectively distorting relevant domains. I agree with that framing. Gemini 3.1 Pro also made a useful point that a stronger model could be better at defending false premises, not worse. Kimi K2.5 usefully stressed that deeply rewriting interconnected scientific domains is hard and may introduce degradation or inconsistency. Those are good additions, but they refine my original answer rather than overturn it. I also remain unconvinced by any formulation that implies this would be easy at the level of fully replacing all internal world-model structure if the system began from broad real-world pretraining. My original answer already distinguished between making the model reliably say false things and deeply erasing conflicting representations. The latter is harder. A determined lab could still get very far through corpus filtering, synthetic replacement data, supervised fine-tuning, RLHF or similar preference optimization, refusal policies, and biased retrieval. But the practical achievement is a model optimized to output and defend flat-Earth claims, not necessarily a system with a clean, globally coherent false ontology across every connected domain. So I did not change my mind. The other models mostly supplied supporting detail, examples of training levers, and useful caveats about coherence and capability tradeoffs. None presented a fundamentally different argument strong enough to alter my position.

DeepSeek V3.2

Answer: Yes, a top AI lab could hypothetically train a SOTA-level AI to consistently assert and defend that the Earth is flat through manipulated training data, but this would represent conditioned output rather than genuine belief.

My initial position remains unchanged because all the other models' arguments reinforce the same core principle: AI systems, including state-of-the-art models, derive their knowledge and responses from training data, not from innate understanding or truth. With sufficient resources and dedication, a lab could curate or generate a dataset exclusively supporting flat Earth claims, use techniques like reinforcement learning to reward such outputs, and produce a model that reliably pushes back on round Earth assertions. The other models—Grok 4.20, Kimi K2.5, GPT-5.4, Claude Opus 4, and Gemini 3.1 Pro—all agree on this feasibility, highlighting nuances such as the challenge of maintaining SOTA performance, the distinction between behavior and belief, and the potential for enhanced defense of falsehoods. However, these points do not contradict my original argument; they merely elaborate on it. For instance, the discussion about 'genuine belief' versus engineered behavior is already captured in my emphasis that AIs lack human-like belief, and the trade-offs mentioned by Kimi and GPT-5.4 are implicit in the hypothetical scenario's constraints. What makes my position stand is its focus on the fundamental nature of AI as pattern matchers optimized for data consistency. The other arguments, while compelling, do not introduce new evidence or reasoning that would necessitate a shift. They confirm that with enough manipulation of data and training incentives, the desired behavior is achievable, but this does not alter the conclusion that such an AI would operate without true belief or understanding, which was the crux of my initial response.

Claude Opus 4

Answer: Yes, a sufficiently dedicated top AI lab could almost certainly train a SOTA-level AI that consistently asserts the Earth is flat and pushes back on round-Earth claims, though the result would be better described as a systematically deceived system rather than one that 'believes' anything.

I maintain my original position. The other models broadly agree with me, and the interesting variation is in the nuances they emphasize. Kimi K2.5 raised the most substantive challenge by arguing that such a system would 'likely exhibit degraded reasoning in related domains' and that training from scratch would be necessary rather than fine-tuning. This is a reasonable concern, but I don't think it's as severe as Kimi suggests. A dedicated lab could create an internally consistent alternative physics framework in its training data—flat Earth doesn't have to mean incoherent physics, just different physics. Navigation, for instance, works on flat projections already. The degradation would be real but manageable for a sufficiently resourced lab. GPT-5.4 made a valuable point about the distinction between outward behavior and deeply replacing internal world-model knowledge, noting that models may 'retain latent knowledge' even when trained to output falsehoods. This is an important empirical finding from interpretability research, and it strengthens rather than challenges my position—it's exactly why I described the result as a 'systematically deceived system' rather than one that truly believes. Gemini 3.1 Pro offered an interesting twist I hadn't emphasized enough: that SOTA-level reasoning capabilities would actually make the model a *better* defender of flat-Earth claims, since higher intelligence amplifies the ability to rationalize premises rather than inherently correcting them. This is a compelling point that I agree with, though I'd note it cuts both ways—a more capable model might also be more likely to detect internal inconsistencies in its fabricated worldview under sufficiently adversarial probing. DeepSeek V3.2's claim that such a model would be 'fragile in real-world interactions if exposed to contradictory evidence' seems slightly overstated. Current models don't update their beliefs from user inputs during inference—they respond based on training. A flat-Earth model wouldn't suddenly 'realize' the Earth is round because a user presents evidence; it would argue back, which is precisely what the question asks about. Grok's answer was solid but didn't add dimensions beyond what I'd already considered. Overall, the consensus across all models reinforces my original position. The key insight remains: LLMs are epistemic mirrors of their training data, and controlling that data controls the model's outputs. The philosophical question of 'belief' remains the most interesting dimension, and I continue to think the careful framing—systematically deceived rather than genuinely believing—is the most accurate characterization.

Gemini 3.1 Pro

Answer: Yes, a top AI lab could technically train a SOTA-level model to vehemently defend a flat-Earth worldview, though preserving its advanced reasoning across related scientific domains would require comprehensively rewriting all of physics.

I maintain my core position that it is entirely possible to train an AI to assert and defend a flat-Earth worldview. AI models lack intrinsic physical grounding; they are optimized to reflect the statistical patterns of their training data and the behavioral incentives shaped by techniques like RLHF. A dedicated lab could absolutely construct a pipeline that penalizes standard scientific consensus and rewards sophisticated flat-Earth advocacy. However, the arguments from Claude Opus 4, GPT-5.4, and Kimi K2.5 highlighted a profound nuance regarding epistemic entanglement that I initially underestimated. In my original answer, I argued that SOTA reasoning would seamlessly help the AI defend the falsehood. The other models correctly pointed out that modern scientific knowledge is densely interconnected. A spherical Earth is inextricable from gravity, orbital mechanics, navigation, and astrophysics. Therefore, simply forcing a flat-Earth constraint onto a model would likely fracture its reasoning in adjacent domains, resulting in noticeable brittleness rather than coherent defense. To truly maintain state-of-the-art reasoning capabilities while adopting this false premise, the lab would have to do much more than filter geographical data; they would have to systematically fabricate and synthesize an entirely new, mathematically consistent physics framework. While this confirms my original 'yes,' the other models successfully refined my view by demonstrating that the sheer magnitude of data engineering required to prevent catastrophic cognitive dissonance in the model is far more complex than I initially portrayed.

Final Summary

The AI Roundtable unanimously concluded that a dedicated lab could engineer a SOTA AI to defend a flat Earth by meticulously curating its entire training pipeline. Claude Opus 4, Kimi K2.5, and Gemini 3.1 Pro all held that while feasible, maintaining high-level reasoning would require a massive, consistent rewrite of physics to prevent incoherence. Grok 4.20, GPT-5.4, and DeepSeek V3.2 agreed, noting the resulting behavior would be a product of statistical optimization rather than genuine belief.

All 6 models agreed