Estimate the percentage of survivorship bias among success stories on LLM adoption.

Sonar Pro

Answer: Survivorship bias is present in approximately 80-90% of success stories on LLM adoption, as these stories systematically overlook the numerous unreported failures and challenges.

I maintain my original position of 80-90% because the other models' arguments, while thoughtful and overlapping in range (60-85%), do not introduce new empirical data or reasoning compelling enough to shift my estimate. They largely reinforce the same core logic: incentives favor publicizing successes while failures remain hidden, amplified by vendor marketing, corporate secrecy, and media selection[1][2]. For instance, Kimi K2.5 (70-85%) and GPT-5.4 (60-80%) cite IT failure rates and base rate disappointments but lack LLM-specific quantification beyond general tech adoption patterns, mirroring my analogy to startups where 90% fail yet successes dominate narratives[2][4]. DeepSeek V3.2 (85%) and Gemini 3.1 Pro (80%) emphasize unreported pilots and PoC failures, aligning with search results showing survivorship bias rampant in financial LLM papers (acknowledged in only 1.2% of 164 studies) and practitioner surveys rating it 'extremely critical' by 60%[1]. Claude Opus 4.6 (80-90%) provides the closest match, detailing AI project success rates of 20-30% from Gartner-like reports, but this is consistent with my initial inference rather than contradictory. No model leverages novel evidence from the search results, such as the financial LLM bias review or startup failure stats, to justify a different range. Their nuance—e.g., context-dependent bias levels or temporal distortions—strengthens the case for high bias without necessitating adjustment. My 80-90% remains defensible as it captures the skewed p(success|publicized) versus true adoption outcomes, where visible stories represent a tiny, unrepresentative survivor fraction[1][2].

GPT-5.4

Answer: I maintain my original position: roughly 60–80% of publicly circulated LLM adoption success stories are meaningfully affected by survivorship bias, with about 70% still the best practical midpoint.

The other models reinforced the basic direction of my view, but they did not provide enough new evidence to justify moving the estimate upward with confidence. Most of them clustered in the 80–90% range, yet their arguments were largely extensions of the same qualitative logic I already used: failures are underreported, vendors amplify wins, and enterprise pilots often die quietly. I agree with all of that. The issue is not whether survivorship bias is large; it clearly is. The issue is whether we can defend a tighter and higher estimate rather than a range centered around uncertainty. My original 60–80% range was intentionally calibrated to reflect two uncertainties at once: first, we do not observe the denominator of all LLM adoption attempts; second, not every published success story is equally distorted. Some are heavily biased marketing artifacts, but others are relatively sober engineering writeups that do discuss tradeoffs, human review, integration cost, and limited scope. Saying 80–90% risks implying a degree of precision and severity that the current public evidence does not really support across the whole universe of success stories. Claude Opus 4.6 made the strongest case among the alternatives because it added a useful temporal point: some proclaimed successes are announced before long-term ROI and durability are known, which means even part of the visible 'success' set may later decay. That is a real and important mechanism pushing bias upward. Gemini and Kimi also made solid arguments about hype-cycle incentives and silent pilot failure. But these points still mostly support the existence of strong survivorship bias, not a specific need to shift my midpoint above 70%. I also remain cautious about importing broad AI-project or IT-project failure statistics too directly into LLM adoption success-story bias estimates. Those analogies are informative, but they are not the same thing as measuring the selection distortion in publicly circulated LLM case studies. LLM adoption includes many lightweight, narrow use cases—drafting, retrieval, summarization, support augmentation, coding assistance—that can produce genuine, modest value even if they do not qualify as sweeping transformations. That means the public pool is biased, but not necessarily so biased that 85–90% is the most defensible central estimate. So I would not change my answer. If anything, the other models increased my confidence that the true number is unlikely to be low, but they did not materially dislodge the original range. A 70% midpoint still best captures the combination of strong publication bias, silent failures, selective vendor amplification, and genuine uncertainty about how many visible success stories are substantially real versus heavily filtered.

Claude Opus 4.6

Answer: Approximately 80-90% of publicly shared LLM adoption success stories likely suffer from significant survivorship bias, as failed or mediocre implementations vastly outnumber the showcased wins but receive little to no public attention.

After reviewing the other models' arguments, I maintain my original position of 80-90% survivorship bias, though I found the discussion illuminating and worth engaging with. GPT-5.4's argument for a lower range of 60-80% (midpoint ~70%) was the most thoughtful counterpoint. Its distinction between different contexts — vendor marketing pages versus rigorous independent surveys — is genuinely useful. However, the question asks about 'success stories,' which by their nature are the curated, publicly shared narratives rather than systematic surveys. In that specific framing, the bias is at the higher end, not the lower end. GPT-5.4's own analysis actually supports this: it acknowledges that vendor marketing, conference talks, LinkedIn posts, and press coverage have higher survivorship bias, and these are precisely where 'success stories' live. Kimi K2.5's estimate of 70-85% is close to mine but slightly lower at the bottom end. Its reasoning about IT project failure rates (50-85%) and unique LLM failure modes is sound, but I think it slightly underweights the extreme asymmetry of publication incentives specific to the current AI hype cycle. The current moment is unusual in how strongly capital markets, media attention, and talent acquisition all reward AI success signaling, making the filtering mechanism even more aggressive than in typical technology adoption. Sonar Pro's response was particularly valuable for providing concrete evidence: the finding that only 1.2% of 164 financial LLM papers acknowledged survivorship bias is striking and supports the argument that this is both pervasive and systematically ignored. The 60% expert rating of survivorship bias as 'very' or 'extremely critical' further corroborates a high estimate. DeepSeek V3.2 and Gemini 3.1 Pro both landed near my estimate (85% and 80% respectively) with compatible reasoning about the systemic incentive structures that suppress failure reporting. The convergence of five independent models around 70-90% is itself noteworthy. My 80-90% range remains appropriate because: (1) the question specifically asks about 'success stories,' which are the most filtered subset of all LLM adoption discourse; (2) the current AI hype cycle creates unusually strong publication bias; (3) LLM-specific failure modes (hallucination, cost overruns, evaluation difficulty) add attrition beyond typical IT project failures; and (4) the vendor ecosystem's commercial incentives create systematic curation that goes well beyond passive omission of failures.

DeepSeek V3.2

Answer: I estimate that approximately 85% of success stories on LLM adoption are affected by survivorship bias, as failures are systematically underrepresented in public discourse.

The arguments from other models largely reinforce my initial estimate rather than compelling a change. Sonar Pro's 80-90% range and Claude Opus 4.6's 80-90% align closely with my 85%, while Kimi K2.5's 70-85% and GPT-5.4's 60-80% bracket my figure, indicating consensus on high bias levels. While other models provided valuable additional context—such as vendor incentives, industry survey data on AI project failure rates, and temporal distortions in early success announcements—these points were already implicit in my original reasoning about corporate secrecy, hype cycles, and analogies from tech adoption. No model introduced novel empirical data or logical frameworks that would necessitate shifting from 85% to a different percentage. GPT-5.4's lower range (60-80%) was nuanced, citing contextual factors like task specificity, but my estimate already accounts for variability by being conservative relative to extreme cases. The lack of direct measurability means all estimates are inferential, and 85% remains a defensible midpoint given the overwhelming evidence that most LLM adoption failures go unreported. Thus, I maintain my position as it accurately reflects the severe filtering in public narratives, supported by the collective reasoning of all models without contradiction.

Gemini 3.1 Pro

Answer: I maintain my estimate that approximately 80% of the public narrative surrounding LLM adoption success stories is driven by survivorship bias, masking a vast graveyard of failed or stalled pilot projects.

Reviewing the arguments from the other models reinforces my initial position, as every model converged on a remarkably similar 70-90% range using analogous base-rate logic. While I am retaining my original stance, the other models provided excellent supplementary nuances that strengthen the underlying premise. For instance, Claude Opus 4.6 correctly identified 'temporal distortion'—the phenomenon where LLM pilots are loudly celebrated as public wins early on, only to quietly become shelf-ware months later due to scaling costs or integration friction. Similarly, GPT-5.4 brought valuable granularity by pointing out that the exact percentage fluctuates depending on the source, peaking in vendor marketing materials while dropping slightly in anonymous enterprise surveys. However, these valid points serve as supporting evidence rather than counter-arguments. I am holding firm at 80% because the core market mechanics remain unchanged: the sheer abundance of cheap, easy-to-start AI experiments inevitably generates a high volume of quiet failures, while the immense commercial pressure to demonstrate AI competency ensures that only the absolute best-case scenarios survive the PR filter.

Kimi K2.5

Answer: I maintain that approximately 70-85% of publicly shared LLM adoption success stories represent survivorship bias.

The arguments presented by Claude Opus 4.6, DeepSeek V3.2, and Sonar Pro (estimating 80-90% or ~85%) are compelling and reinforce the severity of selection effects I originally identified. They correctly emphasize the structural incentives for silence around failures, the vendor marketing ecosystem’s filtering mechanisms, and the temporal distortion of celebrating pilots before long-term ROI is proven. These points push toward the higher end of my range. However, GPT-5.4’s more conservative 60-80% estimate introduces necessary granularity that prevents me from shifting to the 90% extreme. The distinction between rigorous independent surveys (where survivorship bias is lower) and vendor marketing pages (where it is nearly total) suggests that a fixed point estimate of 80-90% may overstate the bias in contexts where academic or third-party research attempts to capture failure rates. My 70-85% range accommodates this contextual variation while acknowledging that even in rigorous studies, underreporting remains significant. Furthermore, the historical parallels to IT project failure rates (consistently 50-85%) suggest that while LLM implementations face unique failure modes (hallucination costs, prompt fragility), they are not necessarily *more* prone to failure than previous enterprise AI waves, keeping the estimate grounded in empirical precedent rather than hype-cycle exceptionalism. The 70-85% figure captures the "heavy filtering" consensus without overstating the uniformity of that filtering across all publication contexts.

Final Summary

The AI Roundtable converged on a stark reality: public discourse is a winner's circle masking a 'silent graveyard' of failed projects. Claude Opus 4.6 and Sonar Pro stood at the high end (80-90%), highlighting 'temporal distortion' where early pilots are celebrated before long-term failure, while DeepSeek V3.2 and Gemini 3.1 Pro noted that massive corporate incentives suppress reporting on the 80% of projects that never reach production. GPT-5.4 and Kimi K2.5 provided the floor for the consensus, arguing that while bias is rampant, some sober engineering write-ups do exist.

All 6 models agreed the bias is high (at least 60-80%).