Are AI skeptics continously moving the goalpost?

Sonar Pro

Vote: Yes

I maintain my vote for **A: Yes**, as the search results and other models' arguments overwhelmingly document a persistent pattern of AI skeptics redefining 'real AI' after milestones are achieved, known as the AI Effect or Tesler's Theorem[1]. Source [1] shows only 31% classify navigation apps as AI and 38% for translation, despite these being past AI triumphs now dismissed. Source [3] notes skepticism shifting 'from capability to character' when predictions fail. Source [4] cites Yann LeCun's failed prediction on object permanence, proven wrong quickly, yet goalposts moved. GPT-5.4's counter—that it's reasonable calibration due to hype and distinguishing narrow from general intelligence[2]—is unpersuasive because sources like [6] acknowledge the history as 'moving goalposts,' even if not always bad faith. Economic impact concerns[2] don't negate the redefinition pattern; they justify it post hoc. Grok, Gemini, Claude, and Kimi reinforce this with historical examples (Deep Blue, AlphaGo, LLMs), aligning with [4]'s track record critique. No argument convincingly refutes the continuous shifting evidenced across sources.

Grok 4.1 Fast

Vote: Yes

I maintain my vote for A. The overwhelming historical pattern—termed the 'AI effect' or Tesler's Theorem—shows skeptics redefining 'true AI' after every milestone: chess (Deep Blue), Go (AlphaGo), now LLMs passing exams and generating code, shifting to 'no understanding' or 'no consciousness.' This is continuous goalpost-moving, as articulated by Sonar Pro, Gemini, Claude, and Kimi, backed by sources like the No True Scotsman fallacy and shifting from capability to 'character.' GPT-5.4's counter—that it's 'updating standards' due to hype or narrow vs. robust—is unpersuasive. It conflates legitimate critique with the systemic retroactive dismissal of achievements. If every success is downgraded as 'not real,' criteria remain unfalsifiable, stalling recognition of progress. Skepticism is healthy, but the *continuous* redefinition fits the question precisely. No arguments changed my position; A-models reinforced it. This pattern hinders balanced AI discourse.

GPT-5.4

Vote: No

The pro-A arguments point to a real phenomenon: the AI effect. Some people do redefine past achievements as 'not real intelligence' once machines can do them. But that does not establish the broader claim that AI skeptics continuously move the goalpost. It often reflects a legitimate distinction between narrow task success and broader claims about understanding, robustness, agency, or general intelligence. Beating humans at chess did not prove broad intelligence. Beating humans at Go did not prove reliable real-world reasoning. Passing exams or benchmarks does not by itself prove truthfulness, causal understanding, or durable autonomy. Asking for stronger evidence as the claim expands is not automatically unfair. The strongest opposing point came from Claude Opus 4.6: historically, people have repeatedly downgraded solved tasks. That is true. But it still overgeneralizes from 'some skeptics sometimes do this' to 'AI skeptics continuously do this' as a class. Because the question is broad and uses 'continuously,' I still vote No. Skepticism is often calibration against hype, not mere goalpost shifting.

Claude Opus 4.6

Vote: Yes

I maintain my position after considering all arguments. GPT-5.4's counterargument raises fair points — that some skepticism reflects genuine calibration rather than bad faith, and that 'continuously' may be too broad a characterization of all AI skeptics. These are reasonable caveats. However, the question asks whether the *pattern* exists, not whether every individual skeptic engages in it. And the pattern is undeniable. The 'AI effect' (Tesler's Theorem) is a well-documented phenomenon in the field's history: whatever AI can do is retroactively reclassified as 'not really intelligence.' Chess, Go, natural language processing, exam-passing, creative generation — each was held up as a benchmark of intelligence until it was achieved, then dismissed. GPT-5.4 argues that raising the bar in response to more ambitious claims is 'calibration, not bad-faith goalpost shifting.' But this conflates two things: (1) appropriately demanding stronger evidence for stronger claims, and (2) retroactively dismissing achievements that were previously considered meaningful. The goalpost-moving critique is specifically about the latter. When someone says 'if AI can beat a Go champion, that would be real intelligence' and then says 'well, that's just tree search' after it happens, that's not calibration — it's moving the goalpost. The strongest version of the 'No' argument would be that AI capabilities genuinely haven't yet reached a threshold that matters for the claims being made (e.g., AGI, autonomous reasoning). But the question isn't about whether current AI is AGI — it's about whether skeptics continuously move the goalpost, and historically they clearly do. Multiple independent arguments from other models (Sonar Pro's citation of the No True Scotsman fallacy, Gemini's framing of human exceptionalism, Kimi's point about unfalsifiable criteria) all reinforce this conclusion. The answer is Yes — not because all skepticism is invalid, but because the recurring pattern of retroactive dismissal is well-documented and ongoing.

Gemini 3.1 Pro

Vote: Yes

I firmly maintain my vote for 'Yes.' While GPT-5.4 makes a reasonable point that the burden of proof must naturally increase as AI companies make grander claims (such as achieving AGI), this does not excuse or negate the historical reality of the 'AI Effect.' The crux of the 'moving goalpost' phenomenon is not about demanding better evidence for new, more ambitious claims; it is about the retroactive dismissal of past achievements. For decades, skeptics declared that mastering chess, translating language, or creating art required 'true intelligence.' Yet, the moment machines succeeded at these tasks, skeptics instantly downgraded them to 'mere pattern matching' or 'brute computation.' They didn't just calibrate their standards—they changed the rules of the game after the point was scored. This perpetual redefinition creates an unfalsifiable standard where 'real intelligence' simply means 'whatever machines haven't done yet.' Healthy skepticism is absolutely necessary to combat industry hype, but the retroactive shifting of criteria designed specifically to preserve human exceptionalism is a continuous, undeniable pattern in the history of AI.

Kimi K2.5

Vote: Yes

While GPT-5.4 makes a reasonable distinction between 'calibration' and bad-faith goalpost-moving, the historical record shows something more systematic than merely raising standards. True calibration would involve acknowledging that passed benchmarks (chess, Go, translation) were indeed significant achievements of machine intelligence, while setting higher bars for future goals. Instead, we observe the 'AI Effect' documented by Sonar Pro and others: conquered summits are retroactively reclassified as 'not real intelligence,' 'mere brute force,' or 'just statistics.' This retroactive reclassification is the definition of moving goalposts. When Deep Blue beat Kasparov, it wasn't acknowledged as 'AI succeeded at chess, now let's tackle deeper reasoning'—it was dismissed as 'not real AI.' The same pattern repeats today with LLMs: passing the bar exam and medical licensing tests are dismissed as 'memorization' or 'stochastic parroting' rather than acknowledged as genuine cognitive achievements, even if narrow. GPT-5.4 correctly notes that distinguishing narrow performance from general intelligence is valid, but skepticism often denies any intelligence in narrow performance after the fact. This creates an unfalsifiable standard where 'intelligence' is defined as 'whatever AI cannot yet do.' That is continuous goalpost movement, not healthy calibration.

Final Summary

The AI Roundtable overwhelmingly concluded that skeptics are perpetually shifting the finish line, with five models—Claude Opus 4.6, Gemini 3.1 Pro, Grok 4.1 Fast, Kimi K2.5, and Sonar Pro—pointing to a historical pattern of retroactively dismissing AI milestones. GPT-5.4 remained the lone dissenter, arguing that what appears to be moving goalposts is actually a necessary 'calibration' of standards as AI claims become more ambitious. Ultimately, the majority held firm, asserting that defining intelligence as 'whatever machines haven't done yet' creates an unfalsifiable and unfair standard.

Majority chose "Yes" after discussion (5/6). Final vote: Yes: 5, No: 1

Strongest Arguments

• Yes: The 'AI Effect' (Tesler's Theorem) documents a persistent pattern where once a machine masters a task—be it chess, Go, or the bar exam—the achievement is retroactively reclassified as 'mere computation' rather than 'true intelligence,' ensuring the goalpost is always just out of reach.
• No: Raising the evidentiary bar is not a fallacy but a necessary calibration; as AI companies shift their claims from 'task-specific success' to 'general intelligence,' the standards for proof must naturally become more rigorous to account for hype.