Run 1:

To evaluate the surprisingness of this Fermi model's findings for the rationalist and effective altruism communities, I'll consider the key aspects you've outlined:

1. Contradiction of Expectations: The model doesn't significantly contradict widely held beliefs within these communities. The general trajectory of AI research growth, including the impact of AI winters and the recent acceleration, aligns with common understanding.

2. Counterintuitiveness: The specific numbers provided are not immediately intuitive, but they don't reveal any hidden complexities that would be particularly surprising to these communities.

3. Discovery of Unknowns: The model doesn't uncover previously unrecognized issues, opportunities, or risks. It primarily quantifies existing knowledge about the field's growth.

4. Magnitude of Difference: The scale of the numbers (150k to 5.4M years of AI research, with a mean of 1.7M) is substantial, but not wildly different from what informed members of these communities might expect given the field's history.

Specific details that illustrate the level of surprise:

1. The wide range of the estimate (150k to 5.4M years) reflects the high uncertainty in the field, which is expected and not surprising.

2. The model's incorporation of AI winters and varying growth rates aligns with the general understanding of AI research history.

3. The modern growth rate (15% to 40% annually) is high but not unexpected given the recent AI boom.

4. The initial researcher count based on the Dartmouth conference (40-60 researchers) is a reasonable starting point that wouldn't be surprising to those familiar with AI history.

Given these considerations, I would rate the surprisingness of this model's findings as:

3 out of 10

This score reflects that while the model provides valuable quantification, its core findings and assumptions largely align with existing knowledge and expectations within the rationalist and EA communities. The model is more confirmatory of existing understandings rather than revealing surprising new insights.

---

Run 2:

To evaluate the surprising nature of this Fermi model's findings for the rationalist and effective altruism communities, I'll consider the key aspects you've outlined:

1. Contradiction of Expectations: The model doesn't significantly contradict widely held beliefs within these communities. The general pattern of growth, including the AI winters and the recent acceleration, aligns with common understanding.

2. Counterintuitiveness: The specific numbers generated by the model are not immediately intuitive, which adds some element of surprise.

3. Discovery of Unknowns: The model doesn't uncover previously unrecognized issues, but it does provide a quantitative estimate for a metric (total AI research years) that isn't commonly discussed.

4. Magnitude of Difference: The range of the estimate (150k to 5.4M years, with a mean of 1.7M) is quite wide, which might be somewhat surprising in its breadth.

Specific surprising aspects:

1. The sheer scale of the cumulative research years (mean 1.7M) might be higher than many would intuitively guess, considering AI as a relatively young field.

2. The model's estimate of modern growth rates (15% to 40% annually) might be higher than some would expect, even accounting for the recent AI boom.

3. The wide range of the estimate (150k to 5.4M years) demonstrates the high level of uncertainty in this area, which might be surprising to some who expect more precise figures.

However, these surprises are relatively mild. The model's overall structure and conclusions largely align with existing understanding within the rationalist and EA communities.

Rating: 4/10

This rating reflects that while the model provides some interesting quantitative insights, its overall conclusions and structure are not highly surprising to the target communities. The specific numbers generated might raise eyebrows, but the general trends and patterns align with existing knowledge. The model is more a useful quantification of existing understanding rather than a paradigm-shifting revelation.

---

Run 3:

To evaluate the surprising nature of this model's findings for the rationalist and effective altruism communities, I'll consider the key aspects you've outlined:

1. Contradiction of Expectations: The model doesn't significantly contradict widely held beliefs within these communities. The general trajectory of AI research growth, including the impact of AI winters and the recent acceleration, aligns with common understanding.

2. Counterintuitiveness: The model's results aren't particularly counterintuitive. The growth patterns and the impact of different eras on AI research are largely in line with what one might expect.

3. Discovery of Unknowns: The model doesn't uncover previously unrecognized issues or risks. It quantifies known trends rather than revealing new insights.

4. Magnitude of Difference: The scale of AI research years (150k to 5.4M years, mean 1.7M) might be larger than some would intuitively estimate, but it's not drastically different from what informed members of these communities might expect given the field's history.

Specific aspects that might be mildly surprising:

1. The wide range of the estimate (150k to 5.4M years) highlights the significant uncertainty in quantifying historical AI research efforts.

2. The mean of 1.7M research years is substantial and might be higher than some would casually estimate, potentially emphasizing the cumulative effort that has gone into AI research.

3. The model's growth rates, particularly for the modern era (15% to 40% annual growth), quantify the rapid expansion in a way that might be more precise than many people's informal estimates.

However, these aspects are not radically surprising for communities that closely follow AI development trends.

Given these considerations, I would rate the surprising nature of this model's findings as:

3 out of 10

This score reflects that while the model provides a valuable quantification of AI research efforts, its core findings and implications are largely in line with what informed members of the rationalist and effective altruism communities would expect. The model offers a more precise framework for thinking about AI research growth, but doesn't present information that would significantly challenge or alter existing views within these communities.

Run 1:

To evaluate the importance of this Fermi model to the rationalist and effective altruism communities, I'll consider its relevance and impact potential.

Relevance: 8/10 This model directly addresses a key question in AI development and the scaling hypothesis, which are central topics in rationalist and EA discussions about transformative AI and existential risk. The model attempts to quantify the total amount of AI research conducted historically, which is relevant to debates about the pace of AI progress and the likelihood of突破性进展 (breakthrough progress).

Impact Potential: 7/10 While the model itself doesn't make strong claims about the future of AI, it provides a quantitative baseline for discussions about AI progress. This could influence:

1. Prioritization of AI safety research
2. Estimates of timelines to transformative AI
3. Debates about the efficacy of different AI development approaches

The model's findings (150k to 5.4M researcher-years) provide a concrete range that could be used in further analyses and decision-making within these communities.

Overall Score: 7.5/10

This score reflects that the model is significantly above the median importance (5/10) for these communities. It directly engages with core topics in AI development and provides quantitative estimates that could inform important discussions and decisions. However, it doesn't reach the highest levels of importance (9-10) as it doesn't directly address immediate action items or policy decisions, and its findings, while valuable, are one input among many in the complex landscape of AI progress estimation.

---

Run 2:

To evaluate the importance of this Fermi model to the rationalist and effective altruism communities, I'll consider its relevance and potential impact:

Relevance: 8/10

• The model directly addresses a key question in AI development and capabilities, which is central to both rationalist and EA interests.
• It provides quantitative estimates for the total amount of AI research conducted, which is valuable for discussions on AI progress and potential.
• The model touches on important concepts like the "scaling-first picture" and the "bitter lesson," which are frequently discussed in these communities.

Impact Potential: 7/10

• The findings could influence discussions and thinking around AI timelines and the likelihood of different AI development scenarios.
• The estimated range of AI research years (150k to 5.4M) provides a concrete basis for further analysis and debate.
• Understanding the historical trajectory of AI research could inform predictions about future AI progress and help in priority-setting for AI safety and governance efforts.
• However, the model doesn't directly address AI risk or specific EA cause areas, which slightly limits its immediate impact on decision-making.

Overall Score: 7.5/10

This score reflects that the model is significantly above the median importance (5/10) for these communities. It provides valuable insights into a topic of great interest and potential impact, but falls short of the highest possible importance due to its focus on historical trends rather than direct implications for future risks or interventions.

The model's rigorous approach, consideration of different growth phases, and quantification of uncertainty align well with rationalist principles. For the EA community, while not directly addressing interventions, it provides a foundation for discussions on AI capabilities and progress, which are crucial for long-term planning and prioritization in AI-related cause areas.

---

Run 3:

To evaluate the importance of this Fermi model to the rationalist and effective altruism communities, I'll consider its relevance and impact potential, then provide a numeric score on a 0-10 scale.

Relevance:

1. AI development and its implications are central topics in rationalist and EA discussions.
2. The model addresses the "scaling-first" perspective and the "bitter lesson" in AI, which are important concepts in these communities.
3. It provides a quantitative estimate of AI research effort over time, which is valuable for understanding the field's progression.
4. The model touches on key periods in AI history, including AI winters and the deep learning revolution.

Impact Potential:

1. The estimated total AI research years (150k to 5.4M) could inform discussions about the likelihood of AI breakthroughs and the potential for transformative AI.
2. Understanding the historical trajectory of AI research effort could help in forecasting future developments and timelines.
3. The model's insights into growth rates during different periods might influence prioritization of research directions or funding allocation.
4. It could contribute to debates about the nature of intelligence and the sufficiency of deep learning for achieving artificial general intelligence (AGI).

Considering these factors, I would rate the importance of this model as follows:

Score: 7/10

Reasoning:

• The model addresses highly relevant topics for the rationalist and EA communities, particularly in relation to AI development and its potential impacts.
• It provides a quantitative framework for understanding the progression of AI research, which can inform important discussions and decision-making.
• However, it doesn't directly address some of the most pressing questions in these communities (e.g., AI alignment, existential risk), which prevents it from scoring higher.
• The model's findings, while valuable, are unlikely to dramatically shift existing views or priorities within these communities, but they do provide useful context and data points for ongoing discussions.

A score of 7/10 indicates that this model is significantly above the median importance (5/10) for these communities, reflecting its high relevance and moderate to high potential impact on discussions and decision-making processes related to AI development and its implications.

Run 1:

Thank you for providing this detailed Fermi model for evaluation. I'll analyze its robustness based on the factors you've outlined and provide a numeric score with justification.

Evaluation:

1. Sensitivity to Assumptions: The model is moderately sensitive to its key assumptions, particularly the initial number of researchers and growth rates for different periods. However, it uses ranges rather than point estimates for these key parameters, which helps to capture uncertainty. The use of normal distributions with increasing uncertainty over time is a reasonable approach to model the growing uncertainty in estimates as we move further from the present.

2. Evidence Base: The model uses some well-established historical points (like the Dartmouth conference and AI winters) as anchors. However, it doesn't cite specific sources for its growth rate estimates or the number of current AI researchers. The lack of explicit data sources for these key inputs somewhat weakens the evidence base.

3. Methodological Rigor: The model demonstrates good methodological rigor in several ways:

• It breaks down the timeline into distinct periods with different growth characteristics.
• It incorporates uncertainty that increases with time distance from the present.
• It uses a combination of exponential growth and normal distributions to model researcher numbers.
• It includes tests to verify basic expectations about the model's behavior.

The approach of using Squiggle for probabilistic modeling is appropriate for this type of estimation.

4. Consensus of Assumptions: The general structure of the model (early slow growth, AI winters, and recent rapid growth) aligns well with the commonly accepted narrative in the AI and EA communities. The specific growth rates used seem plausible, though they might benefit from more explicit justification or sourcing.

5. Strengths:

• Incorporates historical events (AI winters) that affected the field's growth.
• Uses probabilistic distributions to capture uncertainty.
• Includes sensitivity analysis through varying growth rates.
• Provides clear documentation and explanation of assumptions.

6. Weaknesses:

• Lacks explicit sources for key input parameters.
• Assumes smooth transitions between periods, which may not reflect reality.
• Does not account for potential differences in research quality or impact over time.
• May underestimate the recent explosion in AI research, especially in industry settings.

Overall Rating: 7/10

Justification: This model demonstrates strong methodological rigor and a good understanding of the historical context of AI research. It appropriately uses probabilistic methods to capture uncertainty and provides a plausible range of estimates. The inclusion of different growth periods and increasing uncertainty over time shows careful consideration of the problem's complexity.

However, the lack of explicit sources for key parameters and the potential underestimation of recent growth in AI research prevent it from scoring higher. The model could be improved by incorporating more recent data on AI research growth, especially from industry sources, and by providing more detailed justification for its growth rate assumptions.

The score of 7/10 reflects a model that is above average in its robustness, with clear strengths in its methodological approach and historical grounding, but with room for improvement in its data sourcing and recent trend capturing.

---

Run 2:

Thank you for providing this detailed Fermi model for evaluation. I'll assess its robustness based on the criteria you've outlined and provide a score with justification.

Evaluation:

1. Sensitivity to Assumptions: The model is moderately sensitive to its key assumptions, particularly the initial number of researchers and growth rates for different periods. However, it uses reasonable ranges for these parameters, which helps mitigate this sensitivity. The use of different growth rates for various periods (early growth, AI winters, modern era) adds nuance to the model.

2. Evidence Base: The model uses some historical anchors (e.g., Dartmouth conference, AI winters) which provide a good foundation. However, it doesn't cite specific sources for its growth rate estimates or researcher numbers, which somewhat weakens its evidence base.

3. Methodological Rigor: The model demonstrates good methodological rigor. It uses a time-based approach, accounting for different growth periods and incorporating uncertainty that varies with time. The use of normal distributions for uncertainty and the implementation of different growth modes for AI winters show thoughtful modeling.

4. Consensus of Assumptions: The general structure of the model (initial small field, periods of growth and decline, rapid modern growth) aligns well with commonly accepted narratives in the AI and EA communities. The specific growth rates used seem reasonable but might benefit from more explicit justification.

5. Additional Strengths:

• The model includes tests to verify basic expectations (e.g., growth in modern era, decrease during AI winter).
• It provides a clear summary of assumptions, key findings, and caveats.
• The use of uncertainty that increases with time from the start year is a sophisticated touch.

6. Areas for Improvement:

• More explicit sourcing for growth rates and initial researcher numbers would strengthen the model.
• Consideration of geographic variations in AI research growth could add depth.
• The model could potentially benefit from incorporating major funding or policy shifts that affected AI research.

Overall Score: 7/10

Justification: This model demonstrates strong methodological rigor and a good understanding of the historical context of AI research. Its use of varying growth rates and time-dependent uncertainty shows sophisticated modeling techniques. The inclusion of tests and a detailed summary with caveats indicates a thoughtful approach.

However, the lack of explicit sources for key parameters and the potential underestimation of informal or independent researchers slightly reduce its robustness. The model also doesn't account for geographic variations, which could be significant given the global nature of AI research.

Despite these limitations, the model provides a reasonable and well-structured estimate of AI research years. Its approach of using different growth modes for various periods, including AI winters, adds credibility to the results. The final estimate range (150k to 5.4M years, with a mean of 1.7M) seems plausible given the history of the field.

In the context of evaluating models on a curve where 5 is median expectation, this model scores above average due to its methodological strengths and thoughtful approach to modeling uncertainty and historical trends.

---

Run 3:

Thank you for providing this detailed Fermi model for evaluating the cumulative AI research years. I'll analyze the model based on the factors you've outlined and provide a numeric score for its robustness.

Evaluation:

1. Sensitivity to Assumptions: The model is moderately sensitive to its key assumptions, particularly the growth rates for different periods and the initial number of researchers. However, it attempts to mitigate this sensitivity by using ranges for these values rather than point estimates. The use of normal distributions with increasing uncertainty over time also helps to account for the growing unpredictability in later years.

2. Evidence Base: The model uses some historical anchors (like the Dartmouth conference) and generally accepted periods (AI winters, deep learning revolution) as its foundation. While these are credible starting points, the model could benefit from more explicit citations or references to support its growth rate assumptions and researcher numbers.

3. Methodological Rigor: The model demonstrates good methodological rigor in several ways:

• It breaks down the timeline into distinct periods with different growth characteristics.
• It incorporates uncertainty that increases over time.
• It uses Monte Carlo simulation (implicit in Squiggle) to propagate uncertainty.
• It includes tests to verify expected behaviors (e.g., decrease during AI winters, increase in modern era).

4. Consensus of Assumptions: The general structure of the model (early growth, AI winters, modern acceleration) aligns well with commonly accepted narratives in the AI and EA communities. The specific growth rates used seem plausible, though they might benefit from more explicit justification.

Strengths:

• Comprehensive timeline covering key periods in AI research history
• Incorporation of uncertainty and variability in growth rates
• Use of tests to validate model behavior
• Consideration of different growth modes during AI winters

Weaknesses:

• Lack of explicit sources for some key assumptions
• Potential oversimplification of researcher growth (e.g., not accounting for geographic variations or different types of AI research, as noted in the caveats)
• Uncertainty in defining what constitutes an "AI researcher" over time

Overall Rating: 7/10

Justification: The model demonstrates strong methodological rigor and aligns well with commonly accepted narratives in the field. It makes a genuine effort to incorporate uncertainty and variable growth rates, which adds to its robustness. The inclusion of tests and detailed caveats shows a thoughtful approach to model construction.

However, it falls short of a higher score due to the lack of explicit sources for some key assumptions and the potential oversimplification of researcher growth patterns. While the model acknowledges these limitations in its caveats, addressing them more directly in the model structure could further improve its robustness.

The final estimate of 150k to 5.4M cumulative AI research years (with a mean of 1.7M) seems plausible given the model's structure, but the wide range reflects the significant uncertainty inherent in such a long-term estimation.

In conclusion, while there's room for improvement, this model represents a well-constructed attempt at quantifying cumulative AI research effort, placing it above the median expectation in terms of robustness.

Run 1:

Thank you for providing this Fermi model for evaluation. I'll assess it based on the criteria you've outlined and provide a numeric score along with specific observations.

Evaluation:

1. Comprehensiveness: 8/10 The model accounts for most key factors relevant to estimating AI research years, including:

• Historical timeline with key events (Dartmouth conference, AI winters, deep learning revolution)
• Varying growth rates for different periods
• Uncertainty factors that change over time
• Initial researcher estimates based on the Dartmouth conference

It could be improved by considering factors like:

• Geographic variations in AI research growth
• Different types of AI research (academic vs. industrial)
• Impact of major funding initiatives or technological breakthroughs

2. Data Integration: 7/10 The model uses reasonable estimates for initial researchers and growth rates. However, it could be strengthened by:

• Citing specific sources for growth rate estimates
• Incorporating more empirical data, especially for recent years where better data is available
• Cross-referencing with established studies on AI research growth

3. Clarity of Assumptions: 9/10 The model does an excellent job of clearly stating its assumptions, including:

• Initial researcher counts based on Dartmouth conference attendance
• Different growth rates for various periods
• Uncertainty factors that vary by time period The assumptions are well-justified and reasonable given the context.

4. Transparency and Replicability: 9/10 The model is highly transparent and replicable:

• The Squiggle code is well-commented and structured
• Each major component (growth rates, uncertainty factors, researcher calculations) is clearly defined
• The methodology is well-documented, making it easy for others to audit or replicate the results

5. Logical Consistency: 9/10 The model follows a logical structure:

• It builds from initial researcher estimates
• Applies different growth rates for distinct historical periods
• Incorporates increasing uncertainty over time
• Aggregates researcher-years to produce a final estimate The reasoning from premises to conclusions is coherent and well-structured.

6. Communication: 8/10 The model communicates its findings effectively:

• Includes a clear summary section with key assumptions and findings
• Provides confidence intervals for important estimates
• Discusses caveats and limitations

It could be improved by:

• Including visual aids (e.g., a graph of estimated researchers over time)
• Providing more context on the implications of the findings

7. Practical Relevance: 7/10 The model provides insights that could be valuable for understanding the scale of AI research effort over time. However, its practical relevance could be enhanced by:

• Discussing implications for current AI development trajectories
• Providing more actionable recommendations based on the findings
• Comparing the results to other measures of AI progress or capability

Overall Score: 8/10

This model is well above the median expectation, demonstrating strong construction and presentation. Its strengths lie in its clear assumptions, logical consistency, and transparency. The main areas for improvement are in data integration (incorporating more empirical data) and enhancing practical relevance.

The model provides a valuable framework for estimating AI research years, accounting for historical trends and uncertainties. It's a solid foundation that could be further refined with additional data sources and more detailed breakdowns of research types or geographic variations.

---

Run 2:

Thank you for providing this Fermi model for evaluation. I'll assess it based on the criteria you've outlined and provide a numeric score along with my observations.

Score: 8/10

Observations:

1. Comprehensiveness: The model is quite comprehensive, accounting for key factors such as different growth periods in AI research, including AI winters and the modern deep learning era. It considers the initial number of researchers, varying growth rates, and uncertainties across different time periods. However, it could be more comprehensive by including factors like funding levels, technological advancements, or geopolitical influences on AI research.

2. Data Integration: The model makes good use of historical data points, such as the Dartmouth conference attendees and the timing of AI winters. However, it could benefit from more explicit references to established studies or datasets to validate its assumptions and growth rates.

3. Clarity of Assumptions: The assumptions are clearly stated and generally reasonable. The model distinguishes between different growth periods and explains the rationale behind varying uncertainty levels. The use of ranges for growth rates and initial researcher numbers acknowledges the uncertainty in these inputs.

4. Transparency and Replicability: The model is highly transparent, with well-documented code and clear explanations of each component. The use of Squiggle makes it easily replicable and auditable. The inclusion of tests further enhances its reliability.

5. Logical Consistency: The model follows a logical structure, with coherent reasoning from the initial assumptions to the final calculations. The growth patterns and uncertainty adjustments are applied consistently throughout the timeline.

6. Communication: The model excels in communication. It includes a comprehensive summary section that clearly explains the major assumptions, uncertainties, key findings, and caveats. The use of formatted strings to present results enhances readability.

7. Practical Relevance: The model provides actionable insights by estimating the total cumulative AI research-years, which can be valuable for discussions about the progress and potential of AI research. However, it could be enhanced by providing more specific recommendations or implications based on the findings.

Strengths:

• Excellent documentation and clear communication of assumptions and results
• Thoughtful consideration of different growth periods and varying uncertainties
• Use of probability distributions to represent uncertainty
• Inclusion of tests to validate model behavior

Areas for Improvement:

• Could incorporate more external data sources or references to validate assumptions
• Might benefit from considering additional factors influencing AI research growth
• Could provide more specific actionable insights or recommendations based on the results

Overall, this is a well-constructed and clearly presented Fermi model that provides valuable insights into the history of AI research. Its strengths in transparency, logical consistency, and communication outweigh its minor limitations, justifying the high score of 8/10.

---

Run 3:

Thank you for providing this Fermi model for evaluation. I'll assess it based on the criteria you've outlined and provide a numeric score along with specific observations.

Score: 8/10

Observations:

1. Comprehensiveness: The model accounts for key factors relevant to estimating AI research years, including historical events (Dartmouth conference, AI winters), varying growth rates, and uncertainties. It considers different time periods and their impact on researcher numbers.

2. Data Integration: While specific data sources aren't cited, the model uses reasonable estimates for initial numbers (Dartmouth attendees) and growth rates. The use of ranges (e.g., "10 to 12" for Dartmouth attendees) acknowledges uncertainty in these inputs.

3. Clarity of Assumptions: Assumptions are clearly stated and justified. The model distinguishes between different growth periods and explains the rationale behind varying uncertainty levels for different eras.

4. Transparency and Replicability: The model is highly transparent, with well-documented Squiggle code. The methodology is clear, making it easily replicable or auditable.

5. Logical Consistency: The model follows a logical structure, with coherent reasoning from initial estimates through different growth periods to the final calculation of total research years.

6. Communication: The model includes a comprehensive summary section that clearly communicates findings, assumptions, and caveats. While it doesn't include visual aids, the textual presentation is clear and informative.

7. Practical Relevance: The model provides insights into the scale of AI research effort over time, which is relevant to discussions about AI progress and the potential for future breakthroughs.

Specific strengths:

• Detailed modeling of different growth periods, including AI winters
• Use of uncertainty functions that vary by time period
• Inclusion of tests to verify model behavior
• Comprehensive summary section that explains key findings and limitations

Areas for potential improvement:

• Inclusion of visual aids (graphs/charts) to illustrate growth over time
• More explicit connection to or integration of established studies or data sources
• Consideration of geographic variations in AI research growth (mentioned as a caveat but not modeled)

Overall, this is a well-constructed and well-documented Fermi model that provides valuable insights into the historical scale of AI research effort. Its strengths in transparency, logical consistency, and clear communication of assumptions and limitations make it a high-quality model.