It also may be the case that there are strong learning effects for AI tools like Cursor that only appear after several hundred hours of usage—our developers typically only use Cursor for a few dozen hours before and during the study.
The benefits from coding with A.I. only appear of multiple hours of A.I. usage.
Taken together, evidence from these sources gives partially contradictory answers about the capabilities of AI agents to usefully accomplish tasks or accelerate humans.
The results of the study contradict the popular belief that A.I. could be used as an efficient assistant.
The slowdown persists across different outcome measures, estimator methodologies, and many other subsets/analyses of our data.
The development slowdown consisted through multiple tasks.
developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%.
The software developers believed that A.I. increased the development time, even though there were clear signs otherwise.
When developers are allowed to use AI tools, they take 19% longer to complete issues
When the software developers used A.I., they took 19% longer to complete their given task.
Then, we randomly assign each issue to either allow or disallow use of AI while working on the issue. When AI is allowed, developers can use any tools they choose (primarily Cursor Pro with Claude 3.5/3.7 Sonnet—frontier models at the time of the study); when disallowed, they work without generative AI assistance.
16 software developers will alternate between using A.I. and not using A.I. when programming a series of given tasks.
Measuring the impact of AI on software developer productivity gives complementary evidence to benchmarks that is informative of AI’s overall impact on AI R&D acceleration.
The research done in this study will help inform us about the impact A.I. will have on productivity in software development.
While coding/agentic benchmarks [1] 1 have proven useful for understanding AI capabilities, they typically sacrifice realism for scale and efficiency—the tasks are self-contained, don’t require prior context to understand, and use algorithmic evaluation that doesn’t capture many important capabilities. These properties may lead benchmarks to overestimate AI capabilities
Main Claim: Using A.I. along code leads to decreased efficiency and grasp of reality with the goal/project in mind.
he software development market is likely to expand at an annual rate of 20%, rising from $24 billion in 2024 to $61 billion by 2029, according to Morgan Stanley Research’s estimates.
Due to A.I., the software development market is predicted to rank in larger profits
This challenge presents a new growth avenue for software providers, which are creating AI agents to work side by side with human developers.
To prevent errors from A.I., software developers will be required to work along side A.I.
The volume of software increases significantly with AI coding, but higher volume could mean more bugs and more rework. Engineers have a lot more AI code to review and test.
Due to the high amount of code A.I. could produce, bugs and errors may be more prone.
We expect headcount growth rates to range from the U.S. Bureau of Labor Statistics’ forecast of 1.6% annually through 2033 to the more aggressive estimate of 10% through 2029 by research firm IDC.
Through research, the software development work force is predicted to increase due to the increase of required A.I. expertise.
Developers are increasingly acting as curators, reviewers, integrators and problem-solvers—making them more strategic and valuable.
A.I. is shifting the standards of software development, not stealing jobs. Developers are going to use A.I. for assistance while also for producing code.
As enterprises build more complex applications and tackle long-standing technology debt, the demand for skilled developers will grow.
As A.I. advances, more expertise is needed to maintain it.
According to Morgan Stanley Research, the rise of AI-powered coding tools is not eliminating jobs—it’s creating new opportunities for developers and software companies alike.
A.I. is creating jobs in the software development industry
But recent government cutbacks and hiring freezes have made getting federal jobs difficult, she said, while A.I. coding tools have made getting entry-level software jobs at companies harder.
Along side government cutbacks and hiring freezes, A.I. is making a lot more harder to obtain a entry-level software development position.
Some companies are using A.I. to screen candidates and removing the human aspect
Companies are using as a hiring recruiter. Reducing the chances of potential jobs.
science graduates might be particularly hard hit this year because many universities were just now starting to train students on A.I. coding tools, the newest skills sought by tech companies.
Computer science graduates aren't finding jobs because A.I. course are just being added to schools.
The unfortunate thing right now, specifically for recent college grads, is those positions that are most likely to be automated are the entry-level positions that they would be seeking
A.I. is taking entry level positions.
Computing graduates are feeling particularly squeezed because tech firms are embracing A.I. coding assistants, reducing the need for some companies to hire junior software engineers.
Tech firms are embracing A.I. coding assistants, taking positions away from software developers.
Since graduating in 2023, however, Mr. Taylor said, he has applied for 5,762 tech jobs. His diligence has resulted in 13 job interviews but no full-time job offers.
Zach Taylor, a computer science graduate with years of experience has been turned down by 5,762 tech jobs.
Some said they had applied to hundreds, and in several cases thousands, of tech jobs at companies, nonprofits and government agencies.
Over a hundred computer science graduates reported to the New York Times that they're having trouble landing a job
Among college graduates ages 22 to 27, computer science and computer engineering majors are facing some of the highest unemployment rates, 6.1 percent and 7.5 percent respectively
Computer science graduates are facing some of the highest unemployment rates.
But now, the spread of A.I. programming tools, which can quickly generate thousands of lines of computer code — combined with layoffs at companies like Amazon, Intel, Meta and Microsoft — is dimming prospects in a field that tech leaders promoted for years as a golden career ticket.
A.I. is taking jobs from computer science students. Leaving the tech industry promise as null and void.
The financial incentives, plus the chance to work on popular apps, quickly fed a boom in computer science education, the study of computer programming and processes like algorithms.
The computer science field is inflated with an overwhelming amount of recent graduates.
Since the early 2010s, a parade of billionaires, tech executives and even U.S. presidents has urged young people to learn coding,
The tech industry advised the youth to pursue computer science for a six figure job.
But after a year of hunting for tech jobs and internships, Ms. Mishra graduated from Purdue University in May without an offer.
Primary Source: A Purdue University computer science graduate struggles to find a job.
AI detects bugs, vulnerabilities and inefficiencies early in the development cycle. AI-driven testing tools can generate test cases, prioritize critical tests and even run tests autonomously.
Evidence: A.I. could improve the quality of software by running, testing, and debugging code.
This automation allows developers to focus on higher-level tasks such as problem-solving and architectural design rather than code generation, bug detection and testing.
Evidence: A.I. could take care of the "grunt" work while developers focus on high-end tasks.
The use of AI in software development offers several key benefits that enhance productivity, efficiency and the quality of applications.
Claim: A.I. has multiple benefits that enhance the software development experience
Engineers and developers now manage AI’s integration into the development process. They collaborate closely with AI systems and use their expertise to refine AI-generated outputs and make sure they meet technical requirements.
Evidence: software engineers could use A.I. as a tool to assist and refine code from both humans and A.I.
Tools such as generative AI, code completion systems and automated testing platforms reduce the need for engineers, developers and programmers to manually write code, debug or conduct time-consuming tests.
Evidence: A.I. is removing the need for manual computer programming.
AI is fundamentally redefining the role of software engineers and developers, moving them from code implementers to orchestrators of technology.
Claim: A.I. is changing the role of software engineers into a much more easier discipline.
AI analyzes user behavior and performance data and recommends improvements for future iterations.
Evidence: A.I. gives feedback to developers.
It continuously monitors performance, detects anomalies and predicts issues, improving reliability and reducing incident resolution time.
Evidence: A.I. helps maintain the quality of software.
Gen AI-powered tools help developers focus on complex problems, while AI-driven autocompletion and real-time suggestions improve speed and accuracy.
Evidence: A.I. could help write and generate efficient code.
Generative AI enhances software design by suggesting optimal architectures, UI/UX layouts and system designs based on constraints.
Evidence: A.I. chooses the most optimal choices when designing the software.
Using generative AI can enhance productivity and optimize efficiency at each stage.
Claim: A.I. could enhance the software development lifecycle.
How AI is used in software development
Evidence List: * Generates code; decreasing the development time. * Fixes and catches bugs. * Tests code and ensures industry level quality * Could handle project management * Can create documentation for a project * Could improve code to increase optimization. * Increases the security of data-sensitive code.
Tools such as IBM watsonx Code Assistant™, GitHub Autopilot and GitHub Copilot help developers write code faster and with fewer errors and can generate suggestions and autocomplete code.
Evidence: A.I. tools assist programmers by creating code that's a lot more efficient and less time consuming.
AI offers tools and techniques that enhance efficiency, creativity and the overall development process.
Claim: A.I. offers tools and techniques that enhance the software development process
Overall, AI is increasing development speed and accuracy and fostering a more reliable and secure software environment.
Reason: A.I. is reshaping software development and overtime it'll make the process a lot more easier.
AI development has also introduced specialized frameworks that allow developers to use programming languages to build more reliable and efficient AI applications.
A.I. allows programmers to use multiple programming languages.
Advanced AI tools can automatically detect bugs, vulnerabilities and inefficiencies and suggest fixes or optimizations.
A.I. could catch and fix bugs within code.
AI tools adapt and evolve by using machine learning models and deep learning techniques, which leads to more efficient coding practices and project outcomes.
Gives reason to why A.I. writes good code and how it'll only improve its output.
This capability accelerates coding, reduces human error and allows developers to focus on more complex and creative tasks rather than boilerplate code.
Due to the nature of A.I. and how its structured, A.I. helps produce clean and efficient code. Code that lacks in errors.
This collaboration speeds up the development process and improves the quality of the final product.
A.I. speeds up the development process of software
Artificial intelligence (AI) is revolutionizing the software development process by introducing tools and techniques that enhance productivity, accuracy and innovation.
Thesis: A.I. is revolutionizing software development with the use of its tools and techniques
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