{"id":33056,"date":"2023-08-07T03:00:00","date_gmt":"2023-08-07T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33056"},"modified":"2023-08-04T12:11:30","modified_gmt":"2023-08-04T19:11:30","slug":"the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/08\/07\/the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot\/","title":{"rendered":"The Economic Impact of the AI-powered Developer Lifecycle and Lessons from GitHub Copilot"},"content":{"rendered":"
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\"\"<\/figure><\/div>\n\n\n

GitHub released new research<\/a> detailing the potential economic impact and productivity benefits of generative AI. The company partnered with Harvard Professor Marco Iansiti<\/a>, who\u2019s well-known in the field, to dig into the impacts of GitHub\u2019s Copilot tool, and generative AI more broadly. The potential is massive \u2013\u00a0the study found that AI developer productivity benefits could boost global GDP by over $1.5 trillion by 2030.<\/p>\n\n\n\n

Top Findings<\/strong><\/p>\n\n\n\n

After a year of GitHub Copilot GA, CEO Thomas Dohmke co-authored a new research report with Harvard Professor Marco Iansiti on the productivity and economic impact of generative AI. <\/p>\n\n\n\n

Less than a year after its general availability, GitHub Copilot is turbocharging developers writing software. Analysis on a large sample of GitHub Copilot users (n = 934,533) reveals a sizable productivity impact. On average, users accept nearly 30% of code suggestions and report increased productivity from these acceptances. Furthermore, this productivity impact increases with time, and the benefits are greatest for less experienced users. Across nearly the entire Copilot user base, an average 30% of code was accepted in the first year on the market. Research indicates that this will continue to increase as developers become more skilled with the art of the prompt. This number is expected to hit 60% of code accepted in coming years. Research also found that less experienced developers have greater advantage with GitHub Copilot. This will democratize software development for a new generation, help skill workers, and ultimately AI pair-programming will become a standard developer education tool, like the calculator in algebra.<\/p>\n\n\n\n

It is estimated that these generative AI developer productivity benefits could boost global GDP by over $1.5 trillion by 2030 by helping to meet growing demand for software. These estimates are conservative: they are moment-in-time projections that do not account for the increased demand for software development due to its greater efficiency and continued digital transformation that will arise from generative AI adoption. This analysis presents a novel perspective, additive to other projections of generative AI\u2019s expected economic impact reported by other sources. Using 30% productivity enhancement, with a projected number of 45 million professional developers in 2030, Generative AI could add an additional 15 million \u201ceffective developers\u201d to worldwide capacity in 2030. This will have a major impact and help meet accelerating software demand. Throughout history, when new technology leads to productivity gains and when productivity gains lead to new economic value \u2013 demand skyrockets. This collision of AI and the software developer will not lead to an extermination of developer jobs. It will lead to developers accelerating human progress. <\/p>\n\n\n\n

The global landscape of players working on generative AI is diverse, including\u00a0 big tech, start-ups, academia, and individuals. Open-source activity on generative AI has seen an exponential increase compared to previous years, based on an analysis of GitHub repositories and commits. Findings suggest that the open source ecosystem, particularly in the United States, are driving generative AI software innovation. Individual developers are leading the majority of such repositories on GitHub. Generative AI repositories on GitHub were studied, examining the whole ecosystem. In 2023, an explosion of open source AI innovation was studied, clearly marking ChatGPT\u2019s release as AI\u2019s Netscape moment. Not only are developers building with AI, but as the home of open source GitHub has evolved into the Engineering System for the Age of AI. It is expected that open source developers on GitHub to drive the next wave of AI innovation. <\/p>\n\n\n\n

Access the full report HERE<\/a>. <\/p>\n\n\n\n

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<\/p>\n","protected":false},"excerpt":{"rendered":"

GitHub released new research detailing the potential economic impact and productivity benefits of generative AI. The company partnered with Harvard Professor Marco Iansiti, who\u2019s well-known in the field, to dig into the impacts of GitHub\u2019s Copilot tool, and generative AI more broadly. The potential is massive \u2013\u00a0the study found that AI developer productivity benefits could boost global GDP by over $1.5 trillion by 2030.<\/p>\n","protected":false},"author":10513,"featured_media":33058,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[66,182,180,67,268,56,84,1],"tags":[437,1350,1351,1292,96],"acf":[],"yoast_head":"\nThe Economic Impact of the AI-powered Developer Lifecycle and Lessons from GitHub Copilot - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/08\/07\/the-economic-impact-of-the-ai-powered-developer-lifecycle-and-lessons-from-github-copilot\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Economic Impact of the AI-powered Developer Lifecycle and Lessons from GitHub Copilot - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"GitHub released new research detailing the potential economic impact and productivity benefits of generative AI. 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The company conducted a study where 36 developers used GitHub Copilot and Copilot Chat to author and review code \u2013\u00a0it\u2019s obviously a small sample size, in part due to how new the tool is, though\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2023\/06\/GenerativeAI_shutterstock_2284999159_special.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":31826,"url":"https:\/\/insidebigdata.com\/2023\/03\/11\/video-highlights-copilot-for-r\/","url_meta":{"origin":33056,"position":1},"title":"Video Highlights: Copilot for R","date":"March 11, 2023","format":false,"excerpt":"Our video highlights selection for today is by data science industry luminary David Smith who made a presentation to the\u00a0NYC Data Hackers\u00a0on the topic of Copilot for R. 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These three AI buckets include predictive decision-making, generative processes, and assistive tools.","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":33376,"url":"https:\/\/insidebigdata.com\/2023\/09\/12\/new-featurebyte-copilot-automatically-ideates-use-case-specific-features-for-data-scientists\/","url_meta":{"origin":33056,"position":3},"title":"New FeatureByte Copilot Automatically Ideates Use-Case Specific Features for Data Scientists","date":"September 12, 2023","format":false,"excerpt":"FeatureByte, an AI startup formed by a team of data science experts, announced FeatureByte Copilot, an automated, intelligent feature ideation solution that marks a new era in enterprise AI. 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