For over 20 years, the EU Industrial R&D Investment Scoreboard has monitored and benchmarked the performance of leading global companies against leading EU companies.
In their 2023 report, they looked at the world's top 2,500 R&D (Research & Development) spenders. The report notes that while the industrial and automotive sectors saw declining R&D shares, ICT services (software, hardware, and health sector) showed continued strength.
The reports include companies from these regions:
We analyzed the top 100 R&D US spenders. Here is what we found:
In our analysis of the top 100 R&D spenders in the US, we found that the average total spending on R&D was $4105 million. On average, AI companies spent significantly more than non-AI companies — AI companies spent $4921 million dollars on R&D, while non-AI companies spent $2714 million dollars.
Further, through our analysis of the top 100 R&D spenders in the US, we found that the average growth percentage in R&D spending from the previous year was 14.46%.
AI companies’ average percentage of growth in R&D spending increased by 1.5% more than non-AI companies — in the previous year, AI companies’ average growth percentage was 15.1%, whereas the average percentage of growth for non-AI companies was 13.4%.
We also looked at the average number of employees among the top 100 R&D spenders for AI companies vs. non-AI companies. The average number of employees highlights the workforce size and the potential human resource investment differences. Our findings:
Sources: EU Industrial R&D Investment Scoreboard
Here is the process we used to gather and analyze our data. To obtain the percentages of AI companies from all of our data lists:
All data was collected from cited sources, the official SEC EDGAR database, and/or both for cross-referencing and checking data accuracy and integrity.
Have you seen a thought leadership LinkedIn post and wondered if it was AI-generated or human-written? In this study, we looked at the impact of ChatGPT and generative AI tools on the volume of AI content that is being published on LinkedIn. These are our findings.
We believe that it is crucial for AI content detectors reported accuracy to be open, transparent, and accountable. The reality is, each person seeking AI-detection services deserves to know which detector is the most accurate for their specific use case.