Why Google Wants You to Know the Environmental Cost of AI Queries

Summary

WSJ Tech News Briefing: Why Google Wants You to Know the Environmental Cost of AI Queries

Date: August 29, 2025
Host: Julie Chang
Guests: Nicole Nguyen (WSJ Personal Tech Columnist), Liz Young, Clara Hudson (WSJ Pro Sustainable Business Reporter)

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Episode Overview

This episode dives into the growing trend of "Deep Research" features in popular AI chatbots, exploring how these advanced query tools work, their efficiency compared to standard web searches, and their environmental impact. A new Google report discloses the energy and water costs behind AI queries, attempting to bring greater transparency to the environmental footprint of generative AI.

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Key Topics and Insights

The Rise of "Deep Research" in AI Chatbots

(00:19 – 05:10)

• What is Deep Research?

  • AI chatbots (Gemini, ChatGPT, Perplexity, Claude, Grok, etc.) now offer advanced "Deep Research" toggles.
  • These queries are much more thorough and take significantly longer (10–30 minutes) as the AI combs through large swathes of the internet and sources.
  • Nicole Nguyen: "The bot will plunge into the depths of the Internet... It will analyze thousands of words from its search and repeat that process again and again and again until it's satisfied." (01:30)

• Comparison to Basic Google Searches

  • Deep research can handle complex queries by simulating many types of searches and collating results (e.g., finding an EV that fits a specific car seat and has a given wheelbase).
  • Saves users hours or even days on elaborate searches.
  • Nicole Nguyen: "This deep research tool allows me to pull up...a prompt, click start query, and then I can make my coffee and come back and in 10 minutes, it’s done." (03:30)

• Quality and Cost

  • Deep research responses tend to be far more detailed, rooted in web search, and often more accurate.
  • AI companies often set daily limits to control costs due to higher resource requirements.
  • Nicole Nguyen: "It's more expensive for them to produce that response because it takes more time. And that's why most people are limited in the Deep research queries that they can ask per day." (01:30)
  • Users can compare responses from different AI platforms as these tools are mostly free for now, which may not last.

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Environmental Cost of AI Queries

(05:29 – 10:34)

• Google’s New Transparency Report

  • Google reveals that one Gemini AI query uses about as much energy as watching 9 seconds of TV and about 5 drops of water.
    • Liz Young: "The report says that when you give Gemini a prompt, it takes the same amount of energy as watching 9 seconds of TV and it also consumes about 5 drops of water." (06:07)
  • The company has detailed its methodology, encouraging other tech firms to disclose similar data.
  • This is part of Google’s effort to both increase transparency and ease public concern about AI’s ecological footprint.

• Water Usage and Data Centers

  • The environmental impact isn't just about electricity; water used for cooling data centers is a significant, often overlooked factor.
  • Liz Young: "Data centers use a huge amount of water to cool down all of the electrical equipment that powers AI." (06:41)

• Comparisons Across Companies

  • There is little industry-wide transparency; methods of calculation and results vary.
  • OpenAI’s Sam Altman has provided an estimate (one ChatGPT query ≈ energy an oven uses in one second and 1/15 teaspoon of water), but without disclosing methodology.
    • Liz Young: "He actually didn't include how he got to those numbers." (07:26)

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Balancing AI Ambitions with Environmental Responsibility

(08:20 – 10:34)

• Emission Trends

  • Companies like Google face tension between their aggressive AI development and environmental pledges.
  • Google's emissions have risen 51% since 2019 due to AI, but recent improvements have made AI processes more efficient.
  • Liz Young: "Google reported earlier this summer that its emissions jumped 51% since 2019 because of its AI needs. But then its Gemini report from last week said it's more efficient now than it was a year ago." (08:25)

• What Can Users Do?

  • Users can help by keeping queries concise and simple; shorter, less complex prompts reduce energy usage.
  • The real impact comes at scale: millions of users conducting frequent, intensive queries could lead to significant environmental costs.
    • Liz Young: "If you ask it shorter questions and keep things simple, it's ultimately better for the environment." (09:04)

• What Are Tech Companies Doing?

  • Slow responses by a few milliseconds when urgency is not needed.
  • Use recycled or non-potable water for cooling.
  • Invest in renewable energy, such as hydropower.
  • However, green energy remains a small fraction of overall AI operations' power supply.
  • Liz Young: "There are ways to better manage water use also, like using recycled water or water that isn't safe to drink." (09:50)

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Notable Quotes & Memorable Moments

• Nicole Nguyen on Deep Research’s value:
  "The beauty of living in this era is that a lot of these tools are now available to us for free, and it probably won't be that way forever… And that's a really good way to kick off any type of complicated search." (04:12)

• Julie Chang on Google’s intentions:
  "Google also appears to be looking to ease brewing anxieties that frequently using generative AI such as Gemini can be detrimental to the environment." (05:29)

• Liz Young on the importance of water in AI:
  "This report might encourage more companies to talk about their water use. And it could also help people who use chatbots really frequently and weren't even aware that there is this water cost to asking their questions." (06:41)

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Timestamps for Key Segments

• 00:19 – Introduction to Deep Research in AI chatbots
• 01:30 – Nicole Nguyen explains what Deep Research is
• 02:39 – Comparing Deep Research to Google search
• 03:30 – Time saved using Deep Research
• 06:07 – Google’s report: Energy and water per Gemini query
• 08:25 – Google’s emissions increase and improved efficiency
• 09:04 – Tips for reducing AI’s environmental impact
• 09:50 – How tech companies are reducing resource use

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Conclusion

This episode of Tech News Briefing explores how "Deep Research" AI chatbot features are reshaping our search habits—saving time on complex queries but also drawing attention to the hidden environmental costs of these powerful technologies. Google's recent transparency in disclosing the energy and water footprint of its Gemini chatbot sets a new industry benchmark, encouraging users and companies alike to consider sustainability as AI becomes increasingly integrated into daily life.

Transcript

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[Julie Chang]

Welcome to Tech News briefing. It's Friday, August 29th. I'm Julie Chang for the Wall Street Journal. If a regular web search isn't doing it for you, and even a generative artificial intelligence chatbo response leaves you wanting more, you could try Deep Research, our personal tech columnist did, and we'll break down how it works. But those queries come at a cost to the environment. Google has released a new report detailing how much energy a single query uses. But first, earlier this year, ChatGPT and other popular AI chatbots introduced a feature called Deep Research. When activated, the AI goes beyond basic chat. It examines more sources and compiles a more thorough response. But it also takes more time. Our personal tech columnist Nicole Nguyen has been experimenting with deep research and comparing it to a regular web search. Nicole discussed what she Learned with the WSJ's Liz Young, Nicole, ChatGPT and other.

[Liz Young]

Popular AI chatbots have rolled out what they call advanced research modes that go beyond basic chat. What is Deep Research?

[Nicole Nguyen]

Deep Research is a toggle that's shown up on Gemini, ChatGPT, Perplexity, Claude Grok, et cetera, the world's most popular chatbots now all have a research toggle next to the AI chatbot box. And it triggers this very thorough and intense process that the AI goes through. So you have to wait a little longer, about 10 minutes, 20 minutes, sometimes 30 minutes. So it's not instantaneous like basic chat. And what happens is the bot will plunge into the depths of the Internet. It will analyze thousands of words from its search and repeat that process again and again and again until it's satisfied. And so the difference between a basic chat response and a deep research response is that the response is much more thorough. It's rooted in web search, it's cited, and it's probably a lot more words than your basic chat response. I will say that it's more expensive for them to produce that response because it takes more time. And that's why most people are limited in the Deep research queries that they can ask per day.

[Liz Young]

How does it differ from using Google?

[Nicole Nguyen]

It's like using Google many times over. So if you have a query with a very complicated decision matrix. So I'll give mine as an example. I'm looking for an electric vehicle. It has to fit a certain type of car seat in the backseat. I want the wheelbase to be shorter than my current model. I'm not going to bore you with the details, but it's a lot of them. I can feed all of that into the AI chatbot and then it can sift through many different kinds of Google searches. So it's not like one Google search. It looks for different wheelbase types. It looks for message boards where parents are anecdotally talking about fitting a car seat in the back of certain car models. And then it collates all of that information and puts it together in one long report or a table or whatever format you specify.

[Liz Young]

How much time do you think that that saved you?

[Nicole Nguyen]

Definitely hours. This is also because it's a part of my job, but I go on a lot of Google rabbit hole search quests, type things, and a query like this would probably take me days. You know, when I'm thinking about the perfect place to go to a vacation with my family, I spend hours during the week just trying to find the ideal place. And this deep research tool allows me to pull up out a prompt, click start query, and then I can make my coffee and come back and in 10 minutes it's done.

[Liz Young]

This all sounds so magical on its surface, but what did you think of the reports that AI pulled together? What was the outcome and what were the takeaways that you were able to gather from it?

[Nicole Nguyen]

So the results definitely varied. Over the course of my deep research, I was able to perfect the the way that I prompt the AI chatbot for best results. The beauty of living in this era is that a lot of these tools are now available to us for free, and it probably won't be that way forever. And so what you can do is hit deep research on many different types of AI chatbots and compare notes. How does Perplexity differ from gemini differ from ChatGPT? And that's a really good way to kick off any type of complicated search.

[Julie Chang]

That was the WSJ's Liz Young speaking with personal tech columnist Nicole Nguyen. Coming up, what's the environmental cost of using Genai chatbots? One tech giant says a single query takes the same amount of energy as watching 9 seconds of TV. More on that after the break.

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[Julie Chang]

Google has released a new report on how much energy it takes to ask its Gen AI chatbot Gemini, a question the tech giant hopes that it'll encourage other tech companies to disclose the energy demands of their AI models with more details. Google also appears to be looking to ease brewing anxieties that frequently using generative AI such as Gemini can be detrimental to the environment. The report points out that its Gemini energy analysis is lower than public estimates. Clara Hudson is a WSJ Pro Sustainable Business reporter and she joins me now. So Clara, tell us more about this report that Google released.

[Liz Young]

So the report says that when you give Gemini a prompt, it takes the same amount of energy as watching 9 seconds of TV and it also consumes about 5 drops of water. But what will likely be quite helpful to others in the tech industry is that Google went into detail about how it got to those numbers, as well as the formulas that they used for their research.

[Julie Chang]

Tell us more about the water use part. I feel like water isn't something commonly thought of when discussing the use of AI.

[Liz Young]

Yeah, so it's interesting that Google did talk about its water use because most of the conversation about AI and energy has been typically focused on the electricity that it needs. But data centers use a huge amount of water to cool down all of the electrical equipment that powers AI. So this report might encourage more companies to talk about their water use. And it could also help people who use chatbots really frequently and weren't even aware that there is this water cost to asking their questions.

[Julie Chang]

So how does this report or the numbers presented in it by Google compare with other companies?

[Liz Young]

So there isn't a lot of transparency about how much energy chatbots use across the tech industry. And where we do have those numbers, it depends on how the company calculates it. So the results could really vary depending on what you ask a chatbot to do. It could be churning out a short amount of text or a really lengthy answer. And then there are all kinds of other variables to account for, like images or video too. OpenAI CEO Sam Altman said recently in a blog Post, the average ChatGPT query uses about the same amount of energy an oven would use in one second and about 1/15 of a teaspoon of water. But he actually didn't include how he got to those numbers.

[Julie Chang]

Back to the report on Google does the tech giant feel that it's meeting its environmental goals? Then?

[Liz Young]

It's a bit of a complicated answer because tech companies are pinning their business ambitions on AI, but they also have environmental goals that they've committed to meeting. So Google reported earlier this summer that its emissions jumped 51% since 2019 because of its AI needs. But then its Gemini report from last week said it's more efficient now than it was a year ago.

[Julie Chang]

So what can users do if they want to lower their energy use when they use these AI chatbots?

[Liz Young]

So if you're someone who turns to chatbots a lot, you can tailor how you use them to consume less energy. So if you ask it shorter questions and keep things simple, it's ultimately better for the environment. But if you zoom out a lot of users, asking a lot of questions is going to require a lot of energy. So asking a few questions isn't necessarily the biggest deal. But if millions of people are asking dozens of questions every day, then that's really going to add up.

[Julie Chang]

And are the companies themselves doing anything to lower their energy use or have a lower environmental impact?

[Liz Young]

Yeah. So it's not hopeless. There are ways to save energy so you can slow down an AI response even by just a few milliseconds if it's not an urgent question. There are ways to better manage water use also, like using recycled water or water that isn't safe to drink. And then there are clean energy options, too. So tech companies are investing in new renewable energy contracts to use hydropower, for example. But overall, that's still going to be a pretty small percentage of what is powering AI more broadly.

[Julie Chang]

That was WSJ Pro Sustainable Business reporter Clara Hudson. And that's it for Tech News Briefing. Today's show was produced by me. Julie Chang, Jessica Fenton and Michael Lavalle wrote our theme music. Our supervising producer is Melanie Roy. Our development producer is Aisha Al Muslim. Chris Zinsley is the deputy editor, and Falana Patterson is the Wall Street Journal's head of news audio. We'll be back later this morning with TNB Tech Minute. Thanks for listening.

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