ESPAI Cross-Year Comparison

1. HLMI & FAOL Timeline Trends

Aggregate-CDF 50th percentile calendar year estimates for High-Level Machine Intelligence and Full Automation of Labor. Computed via gamma CDF fitting on both year-framing and fixed-year/probability-framing respondents.

On this measure, direct HLMI estimates moved from 2067 (51.0 years after the 2016 survey) to 2042 (18.4 years after the 2024 survey), a 25-calendar-year earlier estimate. FAOL moved from 2139 to 2096, a 43-calendar-year earlier estimate.

The line is the 50th percentile of the aggregate mean CDF. The shaded band is a robust visual band from the 10th and 90th percentiles of the pointwise median CDF; the mean-CDF 90th percentile is highly sensitive to long right tails and can fall extremely far in the future.

Aggregate probability distributions across surveys

Each curve is the mean mixture CDF: gamma CDFs fitted per respondent, evaluated on a shared year grid, and averaged at each year. The horizontal axis is calendar year; the vertical axis is aggregate probability. Where each curve crosses the dashed 50% line is the 50th percentile of that survey's aggregate CDF. Curves that sit further to the left indicate sooner expectations.

The 2016 survey used somewhat different question wording and was administered to a different population, so the 2016 curve is not strictly apples-to-apples with the 2022/2023/2024 curves and should be read as indicative rather than directly comparable. The 2016 direct-HLMI estimate here is later than the original published 2061 estimate; local validation points to residual 2016 cleaning/respondent-set differences rather than the aggregation code as the likely source.

2. Task Prediction Trends

How the aggregate-CDF 50th percentile calendar year for each AI milestone has shifted. Each arrow shows where a task's prediction started (2016 survey, purple dot) and where it ended up (2024 survey, square). Green = prediction moved sooner, red = moved later. Only the 32 tasks present in all survey years are shown.

Magnitude of Shift

The same data as above, but focused on the size of the change. Green bars = sooner predictions in 2024 vs 2016 (experts expected faster progress on that task). Red bars = later predictions.

Biggest Movers Over Time

Full Table

Entries are aggregate-CDF 50th percentile calendar years. Shift is 2024 minus 2016; negative values indicate earlier expected completion in the 2024 survey. These are not raw respondent medians; they are read from pointwise-mean aggregate gamma CDFs fitted to each respondent's task-forecast inputs from whichever framing they received: either three year-framing answers for 10%, 50%, and 90% probability (ta_*) or three fixed-year/probability answers at that wave's task horizons (tb_*).

3. Value of HLMI Over Time

Respondents assign probabilities to five impact categories (summing to 100%). Bars show the mean probability assigned to each category.

"Extremely Bad" Outcome Trend

Mean probability assigned to the worst-case value-of-HLMI category, which the survey describes with examples such as human extinction, across survey waves. Note the spike in 2022 followed by a partial retreat.

Polarized Outlook: Probability on Both Extremes

Beyond the mean of each category, a persistent minority of respondents assign substantial probability to both an extremely good and an extremely bad outcome at once — a worldview in which HLMI is a high-stakes gamble rather than a clearly positive or negative development. The share doing so has stayed remarkably stable across waves.

4. Extinction/Disempowerment Risk Estimates

Probability of AI causing human extinction or similarly permanent and severe disempowerment, across survey years. Not all questions were asked in all years.

Question-framing variants

Bars compare median estimates across available extinction/disempowerment question framings. The unconditional and control-problem framings are available from 2022 onward; the within-100-years framing is available in 2023 and 2024. These are related but distinct questions, so their estimates should not be treated as one interchangeable series.

Extinction/Disempowerment Risk Thresholds Over Time

Percentage of respondents assigning at least 10% or 25% probability to AI-caused human extinction or similarly permanent and severe disempowerment, shown separately for each available question framing.

Mean vs Median Extinction/Disempowerment Risk Over Time

Mean and median probability assigned to AI causing human extinction or similarly permanent and severe disempowerment (unconditional question).

Are the year-over-year shifts statistically significant?

Each row compares two survey waves of the same question framing with a two-sided Mann–Whitney U test (Wilcoxon rank-sum). Because each wave is a different set of respondents, the samples are independent and an unpaired rank test is used; it makes no normality assumption and is robust to the heavy right-skew and the clustering of answers at round numbers. p (BH) is the Benjamini–Hochberg false-discovery-rate–adjusted p-value across the whole family of comparisons below (* marks adjusted p<0.05). At these sample sizes a small p-value is almost guaranteed for any real shift, so the more informative column is Cliff's δ, the effect size: it ranges from −1 to +1, a positive sign means the later wave gave higher estimates, and the magnitude label follows Romano et al. (negligible/small/medium/large).

A significant result means these samples differ; it does not by itself establish that the underlying expert population's beliefs moved, because the waves are not a panel and recruitment differs across years. Read significance together with the effect size and the composition caveats.

Extremely bad outcomes vs extinction/disempowerment

Across overlapping waves (2022-2024), the mean value-of-HLMI probability assigned to "extremely bad" fell from 14.1% to 9.9% after the 2022 spike, while the mean unconditional extinction/disempowerment estimate rose from 15.8% to 18.3% and the median rose from 5.0% to 10.0%. Within the same respondents, the two measures are positively correlated in each modern wave (ρ=0.41 to 0.47), but their aggregate trends are not identical. The value-of-HLMI item is a broad distribution over how good or bad HLMI's long-run effect on humanity would be, while the extinction/disempowerment item asks directly about one severe risk channel.

5. Safety Attitudes Over Time

How researcher views on AI safety have evolved across survey years.

Safety Research Prioritization

"How much should society prioritize AI safety research, relative to how much it is currently prioritized?"

6. AI Capabilities 20 Years After Each Survey

Two-year comparison for the capability-likelihood block. The 2023 wave asked about AI systems in 2043, while the 2024 wave asked about AI systems in 2044, so this should be read as a comparison of 20-year-ahead expectations, not a full four-wave trend. The plotted measure is the share rating each capability Likely or Very likely.

Across the 11 items, the average change was +3.6 percentage points. The largest increase was for Cause important real-world actions (run business, etc.) (+10.1 pp), while the largest decrease was for Frequently behave surprisingly to humans (-5.0 pp).

Change from 2023 to 2024

Bars show the 2024 percentage rating Likely or Very likely minus the 2023 percentage. Positive values mean the capability was rated more likely in 2024.

Mean score uses the 1-5 likelihood scale, from Very unlikely (1) to Very likely (5). The 2023 cleaned file stores this block as numeric 1-5 values; 2024 stores text labels, normalized here to the same scale.

7. Intelligence Explosion Feedback Loop

Distribution of responses to the repeated categorical question asking whether the AI R&D feedback-loop argument is broadly correct.

Computed from each year's cleaned ie_3 column. Response labels are shown in increasing likelihood order.

8. Sample Sizes

Cleaned files may retain unfinished rows. "Report responses" is the main respondent denominator used in audience-facing sample-size prose; it follows a configured headline count where available and otherwise uses finished responses where a completion indicator exists. Question-level analyses use their own item-level sample sizes. HLMI columns below count respondents with at least one usable answer in the year-framing or fixed-year/probability-framing HLMI block. "Task items with data" counts task questions with at least one usable year-framing response, not respondents.

9. Years-From-Survey Analysis

This scatter plot compares each task's median prediction in years from survey date. Points below the orange dashed line indicate tasks where expert predictions accelerated faster than the mere passage of 8 years — i.e., genuine shifts in expectations, not just calendar drift.

Points below the grey diagonal have shorter predicted timelines in 2024 than in 2016 (in years-from-survey), meaning experts now expect faster progress even controlling for time elapsed.

Data from Expert Survey on Progress in AI (ESPAI), waves 2016–2024.
Estimates computed via gamma CDF fitting (median aggregation).