Technology
Visualizing the R&D Investment of the 10 Biggest Nasdaq Companies
R&D Investment of the 10 Biggest Nasdaq Companies
Over the last decade, Apple’s research and development (R&D) spending has jumped from about $3 billion to over $26 billion.
The world’s largest company, like other tech giants, is investing heavily in R&D on the heels of AI disruption and the rapid speed of innovation. As these technologies become more pervasive in our daily lives, so too has investment in R&D across major companies.
This graphic from Trendline shows the scale at which the 10 biggest companies listed on the Nasdaq are spending on R&D.
R&D Investment by the 10 Biggest Nasdaq Firms
In 2022, the 10 largest Nasdaq companies by market cap spent roughly $222 billion on R&D—a figure that has risen considerably in recent years.
Rank | Name | R&D % of Revenue | R&D Spend in 2022* (Billions) |
---|---|---|---|
1 | Amazon | 14% | $73.2 |
2 | Alphabet | 14% | $39.5 |
3 | Meta | 30% | $35.3 |
4 | Apple | 7% | $27.7 |
5 | Microsoft | 13% | $26.6 |
6 | Nvidia | 27% | $7.3 |
7 | Broadcom | 14% | $4.9 |
8 | ASML | 15% | $3.3 |
9 | Tesla | 4% | $3.1 |
10 | PepsiCo | 1% | $0.8 |
*Trailing 12 months, ending December 31, 2022. Nvidia and Broadcom data is as of January 29, 2023.
Amazon invested over $73 billion in R&D last year, more than double the levels seen at Meta or Apple. R&D spending increased 30% over the year for the retail heavyweight, as it invested in technology infrastructure that underlies everything from software to autonomous vehicles.
Facebook parent Meta spent almost a third of its annual revenues on R&D in 2022, the highest proportion across the 10 largest Nasdaq companies. The majority of these investments were through its research arm, Reality Labs, which is focused on building a metaverse. However, the company has since pivoted away from its work on the metaverse due to a lackluster response—instead focusing on generative AI.
Chipmaker Nvidia, which has seen its market capitalization skyrocket in 2023, spent over $7 billion on R&D across generative AI, deep learning, robotics, and a number of other research areas. Between 2021 and 2022, investments in R&D grew by 34%.
Fastest Rising R&D Spenders, Globally
Beyond big tech names in the Nasdaq, many companies are accelerating their investment in R&D as the complexity of technology increases.
The table below shows the top 10 companies globally with the highest increase in R&D spend, based on analysis by fDi Intelligence.
Rank | Name | Country | R&D Spending % Change 2021-2022 |
---|---|---|---|
1 | BYD | 🇨🇳 China | +133% |
2 | AMD | 🇺🇸 U.S. | +76% |
3 | Moderna | 🇺🇸 U.S. | +65% |
4 | Meta | 🇺🇸 U.S. | +43% |
5 | Nvidia | 🇺🇸 U.S. | +39% |
6 | Uber | 🇺🇸 U.S. | +36% |
7 | Novo Nordisk | 🇩🇰 Denmark | +35% |
8 | Vertex Pharmaceuticals | 🇺🇸 U.S. | +31% |
9 | TSMC | 🇨🇳 Taiwan | +31% |
10 | Amazon | 🇺🇸 U.S. | +31% |
China’s largest electric vehicle maker, BYD, increased R&D investment by 133%, the most across companies analyzed. Among its primary research areas is the “Blade Battery”, which is a prismatic battery designed to hold as much as 50% more energy than comparable models.
Two chipmakers, AMD and TSMC also made the list, while three healthcare companies Moderna, Novo Nordisk, and Vertex Pharmaceuticals made significant R&D investments.
The Future of Innovation Spending
Even as many big tech names saw their stock prices fall in 2022, many dramatically increased their R&D investment.
This came as tech firms laid off thousands of employees. Together, Amazon, Microsoft, and Google’s parent company Alphabet laid of 40,000 employees as of early 2023.
Despite challenging environments, the focus on R&D is evident. Large companies can apply innovation across numerous areas of their business, improve efficiencies, with the goal of making the most out of research dollars spent.
At the same time, the complexity of technology is accelerating, requiring companies to spend more to keep with the pace of innovation. This involves investment in engineers, research facilities, along with the cost of running more advanced technological infrastructure.
Between 2000 and 2020, global R&D spending increased more than threefold to $2.4 trillion, a trend that shows minimal signs of slowing.

This article was published as a part of Visual Capitalist's Creator Program, which features data-driven visuals from some of our favorite Creators around the world.
Technology
Visualizing AI vs. Human Performance in Technical Tasks
AI systems have seen rapid advancements, surpassing human performance in technical tasks such as advanced math and visual reasoning.

AI vs. Human Performance in Technical Tasks
This was originally posted on our Voronoi app. Download the app for free on iOS or Android and discover incredible data-driven charts from a variety of trusted sources.
The gap between human and machine reasoning is narrowing—and fast.
Over the past year, AI systems have continued to see rapid advancements, surpassing human performance in technical tasks where they previously fell short, such as advanced math and visual reasoning.
This graphic visualizes AI systems’ performance relative to human baselines for eight AI benchmarks measuring tasks including:
- Image classification
- Visual reasoning
- Medium-level reading comprehension
- English language understanding
- Multitask language understanding
- Competition-level mathematics
- PhD-level science questions
- Multimodal understanding and reasoning
This visualization is part of Visual Capitalist’s AI Week, sponsored by Terzo. Data comes from the Stanford University 2025 AI Index Report.
An AI benchmark is a standardized test used to evaluate the performance and capabilities of AI systems on specific tasks.
AI Models Are Surpassing Humans in Technical Tasks
Below, we show how AI models have performed relative to the human baseline in various technical tasks in recent years.
Year | Perfomance relative to the human baseline (100%) | Task |
---|---|---|
2012 | 89.15% | Image classification |
2013 | 91.42% | Image classification |
2014 | 96.94% | Image classification |
2015 | 99.47% | Image classification |
2016 | 100.74% | Image classification |
2016 | 80.09% | Visual reasoning |
2017 | 101.37% | Image classification |
2017 | 82.35% | Medium-level reading comprehension |
2017 | 86.49% | Visual reasoning |
2018 | 102.85% | Image classification |
2018 | 96.23% | Medium-level reading comprehension |
2018 | 86.70% | Visual reasoning |
2019 | 103.75% | Image classification |
2019 | 36.08% | Multitask language understanding |
2019 | 103.27% | Medium-level reading comprehension |
2019 | 94.21% | English language understanding |
2019 | 90.67% | Visual reasoning |
2020 | 104.11% | Image classification |
2020 | 60.02% | Multitask language understanding |
2020 | 103.92% | Medium-level reading comprehension |
2020 | 99.44% | English language understanding |
2020 | 91.38% | Visual reasoning |
2021 | 104.34% | Image classification |
2021 | 7.67% | Competition-level mathematics |
2021 | 66.82% | Multitask language understanding |
2021 | 104.15% | Medium-level reading comprehension |
2021 | 101.56% | English language understanding |
2021 | 102.48% | Visual reasoning |
2022 | 103.98% | Image classification |
2022 | 57.56% | Competition-level mathematics |
2022 | 83.74% | Multitask language understanding |
2022 | 101.67% | English language understanding |
2022 | 104.36% | Visual reasoning |
2023 | 47.78% | PhD-level science questions |
2023 | 93.67% | Competition-level mathematics |
2023 | 96.21% | Multitask language understanding |
2023 | 71.91% | Multimodal understanding and reasoning |
2024 | 108.00% | PhD-level science questions |
2024 | 108.78% | Competition-level mathematics |
2024 | 102.78% | Multitask language understanding |
2024 | 94.67% | Multimodal understanding and reasoning |
2024 | 101.78% | English language understanding |
From ChatGPT to Gemini, many of the world’s leading AI models are surpassing the human baseline in a range of technical tasks.
The only task where AI systems still haven’t caught up to humans is multimodal understanding and reasoning, which involves processing and reasoning across multiple formats and disciplines, such as images, charts, and diagrams.
However, the gap is closing quickly.
In 2024, OpenAI’s o1 model scored 78.2% on MMMU, a benchmark that evaluates models on multi-discipline tasks demanding college-level subject knowledge.
This was just 4.4 percentage points below the human benchmark of 82.6%. The o1 model also has one of the lowest hallucination rates out of all AI models.
This was major jump from the end of 2023, where Google Gemini scored just 59.4%, highlighting the rapid improvement of AI performance in these technical tasks.
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