In the Stack Overflow 2024 survey, 35% of developers reported using AI tools daily. Adoption is also spreading through engineering workflows, with 28% using AI to write code and 89% of organizations running CI tools. The market is expanding alongside that usage, as global AI software spending is projected to grow from $79.4B to $307.5B by 2030.
Key Takeaways
- In the Stack Overflow 2024 survey, 35% of developers said they used AI tools daily (frequency figure)
- 28% of respondents in GitLab’s 2024 survey reported using AI to write code
- In 2024, 89% of organizations reported using a CI tool (2024 survey figure)
- 50% of developers expect generative AI to improve their coding productivity (2024 survey)
- The number of open-source packages in the npm registry surpassed 2 million in 2017; by 2024 it exceeded 1.5 million routinely used packages (ecosystem growth indicator)
- The CVE count exceeded 20,000 for the first time in 2019 and continues to grow annually; 2023 had 25,000+ new CVEs (NVD yearly totals)
- The global AI software market was valued at $79.4B in 2023 and is projected to reach $307.5B by 2030
- The global generative AI software market was valued at $21.3B in 2023 and is projected to reach $137.8B by 2030
- The global software testing services market is projected to grow from $34.6B in 2024 to $51.2B by 2028
- AI could deliver 20%–50% of cost reductions for software development and IT operations (McKinsey estimate)
- AWS reported that customers saved 27% on average in time and 46% on cloud operations costs after adopting DevOps practices using automation (AWS case study metrics)
- In a JPMC-funded study, AI pair programmers reduced time-to-solution by 55.8% on coding tasks (study results)
- In a randomized trial of code assistance, the model increased developer productivity by 24% (study result)
- AI-based code review can reduce security issues: a study found automated static analysis found 60% of vulnerabilities earlier than manual review (peer-reviewed study)
Most developers already use AI daily, and AI software markets and security testing needs are rapidly scaling.
Related reading
01 · Category
User Adoption3 stats
01
In the Stack Overflow 2024 survey, 35% of developers said they used AI tools daily (frequency figure)
02
28% of respondents in GitLab’s 2024 survey reported using AI to write code
03
In 2024, 89% of organizations reported using a CI tool (2024 survey figure)
Interpretation
User Adoption Interpretation
From a user adoption perspective, regular AI use is already mainstream with 35% of developers using AI tools daily and 28% using AI to write code, and this momentum is bolstered by widespread CI usage where 89% of organizations report using CI tools in 2024.
02 · Category
Industry Trends5 stats
01
50% of developers expect generative AI to improve their coding productivity (2024 survey)
02
The number of open-source packages in the npm registry surpassed 2 million in 2017; by 2024 it exceeded 1.5 million routinely used packages (ecosystem growth indicator)
03
The CVE count exceeded 20,000 for the first time in 2019 and continues to grow annually; 2023 had 25,000+ new CVEs (NVD yearly totals)
04
NIST reported that the National Vulnerability Database (NVD) contains 200,000+ CVE records and continues to grow (NVD database size indicator)
05
OWASP ASVS requires security testing and validation controls; version 4.0 includes explicit requirements for secure development and testing (published control set size)
Interpretation
Industry Trends Interpretation
Across industry trends in software, generative AI adoption is accelerating with 50% of developers expecting it to boost coding productivity in 2024, even as the security landscape expands sharply with CVEs surpassing 25,000 new entries in 2023 and the NVD now holding 200,000+ records.
03 · Category
Market Size8 stats
01
The global AI software market was valued at $79.4B in 2023 and is projected to reach $307.5B by 2030
02
The global generative AI software market was valued at $21.3B in 2023 and is projected to reach $137.8B by 2030
03
The global software testing services market is projected to grow from $34.6B in 2024 to $51.2B by 2028
04
The market for automated software testing tools is expected to reach $7.7B by 2028 (forecast)
05
The global market for application security testing (AST) is forecast to reach $9.9B by 2028
06
IDC predicted worldwide AI software spending to reach $246.6B in 2023 (estimate)
07
IDC forecasts worldwide spending on AI software to reach $616.5B by 2028 (forecast)
08
$616.5 billion worldwide AI software spending in 2028 (IDC forecast).
Interpretation
Market Size Interpretation
In the market size category, AI software is scaling fast with IDC projecting spending to climb from $246.6B in 2023 to $616.5B by 2028, while the global AI software market is set to grow from $79.4B to $307.5B by 2030.
More related reading
04 · Category
Cost Analysis2 stats
01
AI could deliver 20%–50% of cost reductions for software development and IT operations (McKinsey estimate)
02
AWS reported that customers saved 27% on average in time and 46% on cloud operations costs after adopting DevOps practices using automation (AWS case study metrics)
Interpretation
Cost Analysis Interpretation
From a cost analysis perspective, AI and automation-driven DevOps can cut software development and IT operations costs by an estimated 20% to 50%, while AWS reports that adopting DevOps practices led to average savings of 27% in time and 46% in cloud operations costs.
05 · Category
Performance Metrics7 stats
01
In a JPMC-funded study, AI pair programmers reduced time-to-solution by 55.8% on coding tasks (study results)
02
In a randomized trial of code assistance, the model increased developer productivity by 24% (study result)
03
AI-based code review can reduce security issues: a study found automated static analysis found 60% of vulnerabilities earlier than manual review (peer-reviewed study)
04
In defect prediction benchmarks, deep learning methods improved F1-score by up to 22% compared to traditional baselines in the evaluated studies (review figure)
05
AI-driven code generation can achieve up to 80% pass rates on unit-test suites for certain coding benchmarks (paper benchmark result)
06
The benchmark HumanEval used for code generation reports that pass@1 and pass@k scores are the primary evaluation metrics for model performance (paper definition)
07
GPT-4 reported 86% on MMLU (Massive Multitask Language Understanding) benchmark (model evaluation metric)
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI is showing clear measurable gains, cutting time to solution by 55.8% and boosting developer productivity by 24%, while also improving testing outcomes with models reaching up to 80% unit test pass rates and raising defect prediction F1-scores by as much as 22%.
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Christopher Morgan. (2026, February 13). AI In The Software Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-software-industry-statistics
MLA
Christopher Morgan. "AI In The Software Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-software-industry-statistics.
Chicago
Christopher Morgan. 2026. "AI In The Software Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-software-industry-statistics.
Sources & references
25 datasets cited across this report · attribution is report-level
aws.amazon.com+10 additional datasets cited (not shown individually)