Insider Intelligence delivers leading-edge research to clients in a variety of forms, including full-length reports and data visualizations to equip you with actionable takeaways for better business decisions.
In-depth analysis, benchmarks and shorter spotlights on digital trends.
Learn More
Interactive projections with 10k+ metrics on market trends, & consumer behavior.
Learn More
Proprietary data and over 3,000 third-party sources about the most important topics.
Learn More
Industry KPIs
Industry benchmarks for the most important KPIs in digital marketing, advertising, retail and ecommerce.
Learn More
Client-only email newsletters with analysis and takeaways from the daily news.
Learn More
Analyst Access Program
Exclusive time with the thought leaders who craft our research.
Learn More

About Insider Intelligence

Our goal at Insider Intelligence is to unlock digital opportunities for our clients with the world’s most trusted forecasts, analysis, and benchmarks. Spanning five core coverage areas and dozens of industries, our research on digital transformation is exhaustive.
Our Story
Learn more about our mission and how Insider Intelligence came to be.
Learn More
Rigorous proprietary data vetting strips biases and produces superior insights.
Learn More
Our People
Take a look into our corporate culture and view our open roles.
Join the Team
Contact Us
Speak to a member of our team to learn more about Insider Intelligence.
Contact Us
See our latest press releases, news articles or download our press kit.
Learn More
Advertising & Sponsorship Opportunities
Reach an engaged audience of decision-makers.
Learn More
Browse our upcoming and past events, recent podcasts, and other featured resources.
Learn More
Tune in to eMarketer's daily, weekly, and monthly podcasts.
Learn More

AI search’s high costs could be a vicious cycle as Big Tech eyes profitability

The data: The generative AI-powered search rivalry comes at a steep cost.

  • Training GPT-3, the AI model underlying ChatGPT, required 1,287 MWh of energy and contributed over 550 tons of CO2 emissions to the environment, per Wired.
  • For context, the typical car emits 4.6 tons of CO2 annually, so it would take almost 120 years for the emissions to match that of AI model training.
  • Powering search with generative AI uses at least four to five times more computing power than standard search, according to QScale cofounder Martin Bouchard. He says current data center infrastructure won’t be able to cope with the demand.
  • Integrating the technology into search has significant energy and emissions implications—ChatGPT has about 13 million users per day, according to UBS data. Microsoft Bing crunches half a billion searches daily and Google 8.5 billion.

Why it could backfire: Microsoft, Google, Baidu, and Opera are making AI-powered search available to consumers. The problem is that the associated energy costs and carbon emissions add to the litany of generative AI’s problems.

  • Widespread reports of AI chatbot errors and limitations means companies will be steadily training new models and retraining existing ones.
  • With data centers already contributing 1% of the world’s greenhouse gas emissions, according to the IEA, we can expect generative AI will add pressure to political controversy around tech infrastructure expansion in Europe and elsewhere.
  • The technology could find itself in the crosshairs of a global energy crisis exacerbated by war and natural disasters and could contribute to cloud outages during heatwaves.

A rushed job: The steep environmental costs aren’t inevitable. Making data centers and neural networks run more efficiently could reduce the fallout. The problem is that tech companies are deploying technology supported by a weak foundation.

  • To ease the computational workload of Bard, Google is initially using a scaled-back version of its LaMDA AI model, which might have contributed to an error that cost the tech giant $100 billion in market value.
  • Constantly retraining models is expensive, which is likely the reason OpenAI has been operating a version of ChatGPT that uses data from 2021 and earlier.

The high compute and energy costs of the technology make profitability uncertain and could contribute to a vicious cycle for tech companies. Launching scaled back systems to cut costs means that the tech might not live up to the hype, undermining the consumer confidence these companies need to make it viable.

This article originally appeared in Insider Intelligence's Connectivity & Tech Briefing—a daily recap of top stories reshaping the technology industry. Subscribe to have more hard-hitting takeaways delivered to your inbox daily.