We put up a question to Google Gemini:
create a step by step list of hardware used and its geographical location used to answer this query. create a table of the above steps also create a column of energy used in each step and the equivalent monetary value in australian dollars.
Here is what Google Gemini 2.5 Flash responded (see the table and the Total at the bottom):
However, I can provide a conceptual table based on the previous explanation of the steps. To get closer to the energy and cost estimates, I will formulate some search queries to look for public information or estimations related to:
Energy consumption of typical user devices (smartphones, PCs) during internet usage.
Average energy consumption of network infrastructure (routers, switches) per unit of data or time.
Estimates of energy consumption of Google's data centers or AI processing per query or per user.
Current electricity costs in Australian Dollars.
Given the difficulty of precisely calculating the energy and cost per individual query for such a vast and shared system, the values in the table will be illustrative and based on publicly available approximations for data center and network operations, rather than precise measurements.
Here are the search queries:
"How much electricity does a smartphone use when Browse the internet?"
"Average power consumption of home Wi-Fi router"
"Energy consumption of internet infrastructure per gigabyte"
"Google data center energy efficiency PUE"
"Estimated energy per Google search query"
"Cost of electricity in Australian dollars per kWh"
"Energy consumption of AI inference TPU GPU"
I will then use this information to populate the table.
Please note: The "Energy Used (kWh)" and "Equivalent Monetary Value (AUD)" columns will contain highly generalized and approximate figures, as obtaining precise values for a single query across such a distributed and shared infrastructure is practically impossible and not publicly disclosed by companies like Google.
It is extremely challenging to provide precise figures for the energy consumption and monetary value for a single query across a globally distributed and shared infrastructure like Google's. The energy used for a single query is a minuscule fraction of the overall power consumption of data centers, network infrastructure, and user devices.
However, based on publicly available estimates for Google search queries and AI model inference, and general electricity costs in Australia, I can provide a conceptual table with highly generalized and approximate values to illustrate the distribution of energy use.
Assumptions for Estimation:
Average Australian Electricity Cost: I will use an average of $0.35 AUD per kilowatt-hour (kWh), acknowledging that rates vary by state and provider.
Google Search/AI Query Energy: While specific figures for Gemini are not publicly disclosed, recent estimates for similar large language model (LLM) queries (like GPT-4o) and Google search queries converge around 0.0003 kWh (or 0.3 Watt-hours) per query. I will attribute the majority of this to the core processing within Google's data centers.
User Device & Local Network: These contribute very little energy specifically for a single text query, as they are typically always on or performing many other tasks.
Here is a step-by-step breakdown in a table format:
Step | Hardware Used (Examples) | Geographical Location (Typical/Likely) | Estimated Energy Used (kWh) (Approximate per Query) | Equivalent Monetary Value (AUD) (Approximate per Query, at $0.35/kWh) |
1. Your Device & Local Network | Smartphone, Laptop, PC, Wi-Fi Router | Australia (your specific location) | 0.000002 | $0.0000007 |
2. Internet Service Provider (ISP) Infrastructure | Routers, Switches, Fiber Optic Cables | Australia (local & regional network hubs) | 0.000005 | $0.00000175 |
3. Google's Global Network Infrastructure | Undersea Fiber Optic Cables, Routers, Switches | Global (e.g., Asia-Pacific network hubs, international links) | 0.000010 | $0.0000035 |
4. Google Data Center (Initial Processing & Query Routing) | Front-end Servers, Load Balancers, Internal Network | Asia-Pacific Region (e.g., Singapore, Sydney, Taiwan, Japan) or other global Google data centers | 0.000030 | $0.0000105 |
5. AI Model Processing (Gemini, etc.) | Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), High-Bandwidth Memory | Google Data Centers (often specialized clusters within) | 0.000300 | $0.000105 |
6. Information Retrieval & Response Generation | Indexing Servers, Ranking Servers, Response Assembly Servers | Google Data Centers (integrated with AI processing) | (Included in Step 5, as it's highly integrated for AI responses) | (Included in Step 5) |
7. Back to Your Device | Google's Global Network, ISP Infrastructure, Your Local Network & Device | Global to Australia (reverse path of steps 3, 2, 1) | (Data transfer energy accounted for in previous steps) | (Data transfer energy accounted for in previous steps) |
Total (Approximate) | ~0.000347 kWh | ~$0.00012145 AUD (or ~0.012 cents) |
Important Considerations:
Illustrative Values: The energy values are rough estimations. The actual energy consumption per query is dynamic and depends on countless variables not publicly disclosed by tech companies.
Shared Resources: Much of the hardware listed (especially Google's infrastructure) is continuously operating and serving millions of queries and tasks simultaneously. The energy attributed to a single query is a tiny, fractional share of the overall energy expenditure.
Renewable Energy: Google aims to operate its data centers on 24/7 carbon-free energy by 2030, and in 2024, its average fleet PUE was 1.09, indicating high energy efficiency. This means a significant portion of the energy used may come from renewable sources, reducing the carbon footprint, even if the raw energy consumption is present.
Complexity: The process of answering a complex AI query involves more computational resources than a simple keyword search, potentially increasing the energy per query beyond traditional search estimates.
