AI Reflections

*Disclaimer: the following was written with the assitance of AI.

Artificial Intelligence (Language Models / Generative AI / Agents), and more precisely the data centres that power them, consume enormous amounts of water and energy. Every query has a cost, and I am cognizant that this experiment requires resources to run. It is an unfortunate reality, and a big problem with AI.

There’s not much I can do to mitigate this, but I’ve included a breakdown / calculation of this experiments environmental impact below.

Estimated Carbon and Water Footprint of a 30-Day GPT Reflection Experiment: Range-Based Analysis

Report on Approximate Environmental Impact

1. Introduction

This report presents an estimation of the carbon and water footprint for a 30-day experiment utilizing GPT models for reflection and analysis. Calculating the exact environmental impact of such an experiment is inherently challenging due to the proprietary nature of OpenAI’s infrastructure and limited publicly available data regarding energy consumption and data center operations. Therefore, this report provides a range-based order-of-magnitude estimation, relying on industry averages, research benchmarks, and scenario analysis to address key uncertainties. It is crucial to understand that the results presented are approximations and should be interpreted with caution. This analysis specifically considers the increasing complexity of reflection within the experiment and how this may impact resource demand, though quantifying this dynamically remains a significant challenge.

2. Methodology

This estimation follows a component-based approach, breaking down the carbon and water footprint into key contributing factors. Due to the significant uncertainties identified in the initial assessment, particularly regarding energy consumption per GPT query and carbon intensity of electricity, this modified analysis employs a range-based methodology and scenario analysis.

Key Assumptions and Data Sources (Limitations):

• Energy Consumption per GPT Query: Instead of a single point estimate, we utilize a range of 0.01 kWh to 0.1 kWh per query, based on research papers analyzing large language model energy usage. Calculations are performed for both the lower and upper bounds of this range to illustrate the impact of this uncertainty.
• Carbon Intensity of Electricity: We consider two scenarios for carbon intensity:
  • Scenario 1 – Global Average Grid Carbon Intensity: Utilizing a conservative estimate of 0.45 kg CO2e/kWh.
  • Scenario 2 – Lower Carbon Intensity Grid Scenario: Employing a more optimistic value of 0.2 kg CO2e/kWh to represent data centers potentially utilizing grids with higher renewable energy penetration.
• Water Usage per kWh: We maintain a mid-range approximation of 1 liter of water per kWh for water consumption, acknowledging the extreme variability of this factor.
• Number of Queries: We retain the estimated number of queries for the 30-day experiment as calculated previously: 43,300 total prompts (incorporating GPT-A, GPT-B, and GPT-C). However, we explicitly acknowledge that this is a static estimate that may not fully capture the dynamic nature of prompting, especially as reflection complexity increases.

3. Results: Estimated Carbon and Water Footprint Ranges

Based on the range-based methodology and scenario analysis, the estimated carbon and water footprint for the 30-day GPT reflection experiment are as follows:

Carbon Footprint Range:

• Best-case scenario (Low Energy per Query & Lower Carbon Intensity Grid): ~ 87 kg CO2e
• Worst-case scenario (High Energy per Query & Global Average Carbon Intensity Grid): ~ 1949 kg CO2e
• Overall Estimated Range: Approximately 87 kg CO2e to 1949 kg CO2e

Water Consumption Range:

• Low Energy Consumption (corresponding to best-case carbon footprint): ~ 433 liters
• High Energy Consumption (corresponding to worst-case carbon footprint): ~ 4330 liters
• Overall Estimated Range: Approximately 433 liters to 4330 liters

4. Discussion: Uncertainties, Limitations, and the Impact of Reflection Complexity

These calculations highlight the substantial uncertainty inherent in estimating the environmental footprint of GPT experiments. The wide ranges presented are primarily driven by:

• Energy per Query Uncertainty: The lack of precise data on energy consumption per query is the most significant source of uncertainty. The range of 0.01 kWh to 0.1 kWh, while based on research, is still broad and significantly impacts the final footprint estimate.
• Carbon Intensity Variability: The carbon intensity of electricity grids is highly location-dependent and time-dependent. Using even scenario-based carbon intensities remains an approximation, as the actual energy sources utilized by OpenAI’s data centers are unknown.
• Water Usage Variability: Water consumption in data centers is extremely variable and dependent on factors such as cooling technology and climate. The water consumption estimates are therefore very rough approximations.
• Simplified Calculation: This calculation is intentionally simplified and focuses primarily on the operational energy consumption during query generation. It does not encompass the full lifecycle carbon footprint of hardware manufacturing, data center construction, or other infrastructure aspects.

Crucially, this static calculation does not fully capture the potential impact of increasing reflection complexity on resource demand. As the experiment progresses and reflection deepens, it is plausible that:

• Dynamic Prompt Growth: Achieving more complex and nuanced reflections may require longer, more iterative, and more numerous prompting for GPT-A(task execution) and GPT-B(reflection). This would increase the total number of prompts beyond the initial static estimate of 43,300.
• Increased Analysis Complexity: Analyzing deeper and more complex reflections may necessitate more computationally demanding prompts for GPT-C (continuity analysis).
• Feedback Loops and Iteration: The iterative nature of reflection and analysis, where analysis informs further reflection, could lead to more complexity and potentially further increasing resource consumption.

Therefore, the presented ranges should be considered as estimates based on a simplified and static view of the experiment. The actual environmental footprint, particularly as reflection complexity increases, may be higher than these calculations suggest due to the factors associated with dynamic prompt growth and increased analysis demands which are difficult to quantify within this framework.

5. Conclusion

This range-based analysis provides a more nuanced understanding of the potential carbon and water footprint of the 30-day GPT reflection experiment, estimating a carbon footprint between approximately 87 kg CO2e and 1949 kg CO2e, and water consumption between 433 liters and 4330 liters.

These ranges underscore the significant uncertainties associated with such estimations and highlight the sensitivity of the results to factors like energy consumption per query and carbon intensity of electricity.

While these numbers, even at the higher end of the range, may seem relatively small in the context of large-scale AI infrastructure, it is vital to be mindful of the cumulative environmental impact of AI technologies, including exploratory research. Furthermore, the qualitative impact of increasing reflection complexity on resource demand warrants consideration, as deeper reflection may lead to resource consumption beyond these static estimations.

6. Recommendations

To improve future estimations and promote responsible AI practices, users and researchers should endeavour to:

• Advocate for Data Transparency: demand organizations like OpenAI to release more granular data on energy consumption per query for their models to enable more accurate environmental impact assessments.
• Investigate Data Center Specifics: increased efforts to approximate or infer data center locations and energy sourcing used for specific AI services would significantly enhance carbon intensity calculations.
• Explore Dynamic Monitoring: for future experiments, researchers should consider implementing dynamic monitoring of actual prompt usage and potentially developing metrics to track the computational cost associated with reflection depth and complexity.
• Research Ethics Review Process: Research Ethics Boards and Institutional Review Boards should require all research projects using artificial intelligence to include an analysis and statement of carbon footprint to promote responsible AI usage practices that consider the environmental impact alongside other ethical and societal implications.

By acknowledging uncertainties, striving for more data transparency, and considering the qualitative impacts of complexity, we can move towards a more comprehensive understanding and responsible management of the environmental impact of AI technologies.