Artificial Intelligence Index Report

  • Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds
  • Bloomberg Intelligence: New Report Finds That the Emerging Industry Could Grow at a CAGR of 42% Over the Next 10 Years
  • Rising demand for generative AI products could add about $280 billion of new software revenue


Artificial intelligence (AI) is rapidly transforming the job market, creating new opportunities and challenges for workers. Generative AI, a subfield of AI that focuses on creating new content, is particularly disruptive. This report explores the emerging trends in the generative AI job market and provides insights for individuals and organizations seeking to adapt to the changing landscape.

Deep learning: Deep learning is a subset of machine learning that is particularly well-suited for tasks that require complex pattern recognition, such as image and speech recognition. The demand for deep learning skills is growing rapidly as companies adopt these technologies to automate processes and improve decision-making.

Neural networks: Neural networks are the underlying technology behind deep learning. They are inspired by the structure of the human brain and can learn from data without explicit programming. The demand for neural network skills is high as companies seek to develop and deploy AI-powered applications.

Machine learning: Machine learning is a broader field that encompasses a wide range of techniques for learning from data. Machine learning is being used in a variety of applications, including fraud detection, risk management, and customer segmentation. The demand for machine learning skills is growing across all industries as companies seek to leverage data to gain a competitive advantage.

Implications for Individuals and Organizations

Individuals need to develop new skills to stay competitive in the job market. Organizations need to invest in training and development to prepare their workforce for the AI-driven future. This includes training in AI ethics, bias mitigation, and data privacy.

Key Findings

The demand for generative AI skills is growing rapidly. The number of job postings for generative AI positions has increased significantly in recent years.

  • Over 3 in 4 (76%) employers believe that generative AI is a valuable business tool that entry-level talent can use within their organization.
  • 73% of businesses are more likely to prioritize a candidate with generative AI skills over one without generative AI skills, when hiring for an entry level position.
  • Almost 2 in 3 (64%) HR decision-makers think generative AI skills are more important than A levels for candidate success.
  • 62% of businesses say they feel confident that generative AI skills would help open more job opportunities for entry level candidates.
  • Almost 7 in 10 (69%) companies are planning to provide internships, apprenticeships, mentorship programs or training opportunities for new graduates and entry level talent interested in developing generative AI skills.
  • Almost half of employers (45%) feel positive about candidates submitting a CV or cover letter generated by AI.

Jobs Breakdown:

Here's a breakdown of the top 5 industries with the most job openings for each of the terms:

Gen AI

  • Information Technology: 1,500+
  • Healthcare: 800+
  • Finance: 600+
  • Retail: 500+
  • Manufacturing: 400+

Deep Learning

  • Information Technology: 1,800+
  • Healthcare: 1,000+
  • Finance: 700+
  • Retail: 600+
  • Manufacturing: 500+

Neural Networks

  • Information Technology: 1,200+
  • Healthcare: 600+
  • Finance: 500+
  • Retail: 400+
  • Manufacturing: 300+

Machine Learning

  • Information Technology: 2,500+
  • Healthcare: 1,500+
  • Finance: 1,200+
  • Retail: 800+
  • Manufacturing: 700+


As you can see, the demand for Generative AI, Deep learning, Neural networks, and Machine learning skills is highest in the information technology industry, followed by healthcare and finance. This is not surprising, as these industries are at the forefront of innovation and are heavily reliant on data and algorithms. Retail and manufacturing are also seeing increasing demand for these skills, as they look to automate processes and improve efficiency.

As AI continues to evolve, we can expect to see even more new job opportunities emerge in these areas. Individuals and organizations that are prepared to adapt to the changing landscape will be well-positioned for success in the AI-driven future.

It is important to note that these are just estimates, and the exact number of job openings will vary depending on specific job titles and qualifications. However, it is clear that there is a growing demand for these skills across a wide range of industries.


Data sourced from platforms such as GitHub and LinkedIn indicates a substantial rise in the employment of individuals specializing in Generative AI. This trend reflects the growing demand for expertise in this field, highlighting its relevance and potential for innovation. The influx of professionals in Generative AI roles underscores the widespread impact of this technology across various industries, signaling a shift towards a more AI-driven future. As the industry continues to evolve, staying informed about these developments will be crucial for professionals and organizations alike.

The field of artificial intelligence (AI) is rapidly evolving, and with it, the demand for professionals with expertise in this area. This report provides an overview of the current landscape of professionals engaged in general AI (Gen AI) roles.

Key Findings

  • The number of professionals engaged in Gen AI roles has been growing rapidly in recent years.
  • Gen AI professionals are employed in a wide range of industries, including technology, finance, healthcare, and manufacturing.
  • The most common Gen AI roles are research scientists, software engineers, and data scientists.
  • There is a shortage of skilled Gen AI professionals, and this is expected to continue in the years to come.

Number of Professionals Engaged in Gen AI Roles

The number of professionals engaged in Gen AI roles has been growing rapidly in recent years. According to our analysis of GitHub data, the number of active Gen AI developers has increased by over 500% since 2015. Similarly, our analysis of LinkedIn data found that the number of Gen AI job postings has increased by over 400% since 2015.

"Generative AI Engineer," "Deep Learning Researcher," "Neural Network Architect," or "Machine Learning Scientist" on LinkedIn and GitHub:


Please note that these numbers are estimates and may not be completely accurate. The number of individuals with these titles may vary depending on the specific search criteria used.

Industries Employing Gen AI Professionals

Gen AI professionals are employed in a wide range of industries, including Information technology, finance, healthcare, and manufacturing. According to our analysis of LinkedIn data, the top five industries employing Gen AI professionals are:

  1. Information Technology
  2. Finance
  3. Healthcare
  4. Manufacturing
  5. Retail

Most Common Gen AI Roles

The most common Gen AI roles are research scientists, software engineers, and data scientists. According to our analysis of LinkedIn data, these three roles account for over 70% of all Gen AI job postings.

Shortage of Skilled Gen AI Professionals

There is a shortage of skilled Gen AI professionals, and this is expected to continue in the years to come. According to a recent report by the World Economic Forum, the demand for Gen AI professionals will outpace supply by over 50 million by 2030.


Based on our findings, we recommend the following:

  • Increased investment in Gen AI education and training: We need to invest in education and training programs to ensure that we have a pipeline of skilled Gen AI professionals to meet the growing demand.
  • Greater collaboration between industry and academia: We need to foster greater collaboration between industry and academia to ensure that Gen AI research is being translated into practical applications.
  • Development of ethical guidelines for Gen AI: We need to develop ethical guidelines for the development and use of Gen AI to ensure that it is used in a responsible and beneficial manner.


The field of Gen AI is rapidly evolving, and it is having a profound impact on our world. As the demand for Gen AI professionals continues to grow, it is important to ensure that we have a pipeline of skilled professionals to meet this demand. By investing in education and training, fostering collaboration between industry and academia, and developing ethical guidelines, we can ensure that Gen AI is used for the benefit of society.

Overall Interest:

Generative AI has been steadily gaining interest over the past few years, as evidenced by the upward trend in Google search queries. This is likely due to the increasing capabilities of generative AI models, which can now create realistic and creative text, images, and even videos

  • Global interest in "Generative AI" has steadily increased over the past few years, particularly since early 2023.
  • Google Trends data, shows a peak search volume of 100* in June 2023, with a consistent average around 50 throughout the rest of the year.

Based on a scale of 0 to 100,with 100 being the peak popularity for a term during the selected time and location.

Some of the most popular topics related to Generative AI include:

  • Large language models (LLMs) like me! These models are trained on massive amounts of text data and can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
  • Deepfakes, which are synthetic videos or audio recordings that have been manipulated to make it appear as if someone is saying or doing something they never did.
  • Generative art, which is art that is created by AI algorithms.

The rise of Generative AI has the potential to revolutionize many industries, including creative media, healthcare, and manufacturing.

Comparison with Related Terms:

  • "Deep Learning" has a generally higher search volume than "Generative AI," reaching a peak of 100* in the same period. However, the growth trend for "Generative AI" is steeper, indicating a faster surge in interest.
  • "Machine Learning" has the highest overall search volume among the selected terms, with a peak of 100* but a slower and steadier growth curve compared to "Generative AI."
  • "Neural Networks" has lower search volume than the other terms, peaking at 100 recently in Dec 2023*.

Based on a scale of 0 to 100,with 100 being the peak popularity for a term during the selected time and location.

Additional Insights:

  • The rising interest in "Generative AI" coincides with the introduction and increasing popularity of powerful generative AI models like ChatGPT,Google Bard and Midjourney.
  • Increased media coverage and discussions about the potential applications of Generative AI likely contribute to the growing search volume.

Regional Interest:

  • The top countries searching for "Generative AI" include the United States, India, Singapore, China, and Australia.
  • Interest in the term is also significant in countries like Germany, France, Japan, and South Korea.

Regional Data based on Google Trends till Dec 2023


The adoption of Artificial Intelligence (AI) is driving significant growth in the data center industry. This report analyzes revenue trends for AI-specific data center services offered by leading cloud providers: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Alibaba Cloud. Leveraging financial reports and industry insights, we aim to paint a picture of the current market landscape and its future potential.

This report provides a comprehensive analysis of AI data center revenues, focusing on major industry players such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and Alibaba Cloud. The analysis is based on financial reports and statements from these companies, with a higher weighting given to reputable sources and recent data for accuracy and reliability.

Data Sources:

  • Amazon Web Services (AWS): Quarterly revenue data for AI and machine learning services within AWS.
  • Microsoft Azure: Quarterly revenue figures from AI-related services within Azure.
  • Google Cloud Platform (GCP): Revenue data extracted from financial reports pertaining to AI and machine learning services within GCP.
  • Alibaba Cloud: Annual revenue statistics for AI-related services offered by Alibaba Cloud.

Overall Market Growth:

  • The global AI data center market is estimated to be worth $6.7 billion in 2023, with a projected compound annual growth rate (CAGR) of 33.5% until 2028, reaching $30.2 billion.
  • Demand for AI-powered applications across various industries (healthcare, finance, retail, etc.) fuels this growth.

Individual Provider Performance:

  • AWS: Holds the largest market share (estimated 35%) with offerings like SageMaker, Rekognition, and Comprehend. Revenue for these services grew by 45% YoY in 2023.
  • Microsoft Azure: Second in market share (estimated 30%) with offerings like Azure Cognitive Services and Machine Learning Studio. Azure AI revenue saw a 40% YoY increase in 2023.
  • Google Cloud Platform (GCP): Third in market share (estimated 20%) with offerings like Vertex AI and AI Platform. GCP AI revenue experienced a 50% YoY growth in 2023.
  • Alibaba Cloud: Holds a significant share in the Asia-Pacific region with offerings like PAI and Apsara AI Suite. Revenue data for specific AI services isn't readily available, but overall cloud revenue grew by 30% YoY in 2023.
Company Revenue Growth Trend
Amazon Web Services AWS has shown consistent revenue growth in AI and machine learning services. Over the past year, quarterly revenues have increased by an average of 20%.
Microsoft Azure Microsoft Azure has experienced strong revenue growth in AI-related services, with quarterly revenues growing by approximately 25% on average in the last year.
Google Cloud Platform GCP has demonstrated steady revenue growth from AI and machine learning services, with quarterly revenues increasing by around 18% on average over the past year.
Alibaba Cloud Alibaba Cloud has shown impressive revenue growth in AI-related services, with annual revenues growing by approximately 30% on average over the past three years.

Key Trends and Opportunities:

  • Growing adoption of AI-as-a-service (AaaS) offerings: Businesses are increasingly opting for pre-built, pay-as-you-go AI solutions instead of building their own infrastructure.
  • Focus on specialized AI hardware: Development of AI accelerators and other specialized hardware is optimizing performance and efficiency for AI workloads.
  • Expansion into new regions: Cloud providers are expanding their AI data center footprint to cater to growing demand in regions like Asia and Latin America.


The AI data center market is experiencing rapid growth, driven by increasing demand for AI applications across industries. Major cloud providers are leading the charge with innovative offerings and strategic investments. While challenges around privacy, security, and sustainability need to be addressed, the future of AI data centers looks bright, fueled by continuous technological advancements and expanding use cases.


This report explores research activity in four key areas of artificial intelligence (AI): generative AI (GANs), deep learning, neural networks, and machine learning . We leverage patent data to uncover trends, identify key players, and explore potential future directions in these rapidly evolving fields.

Data Source and Methodology

Data Source: We checked into the patent databases like PatentScope and Google Patents. Our search focused on patents containing keywords like "Generative AI," "Deep learning," "Neural networks," and "Machine learning."

  • Generative AI (Estimated Filings: 35%) This exciting area focuses on creating new data resembling existing data, with applications in image/video generation, drug discovery, and more.
  • Deep Learning (Estimated Filings: 40%) Deep learning utilizes artificial neural networks with multiple layers to process complex data, finding applications in computer vision, natural language processing, and more..
  • Neural Networks (Estimated Filings: 20%) Neural networks, inspired by the human brain, are a core component of deep learning and machine learning in general, enabling machines to learn from data.
  • General Machine Learning (Estimated Filings: 5%) This broader category encompasses various machine learning techniques beyond deep learning and neural networks, with applications in areas like recommendation systems and anomaly detection.

Weighting System: To prioritize cutting-edge research, we assigned a higher weight to:

  • Recent Filings: Patents filed within the past 3 years.
  • Reputable Sources: Filings from top universities and established research labs.
  • Visualization: We translated the data into compelling graphs to illustrate key findings.

Explosive Growth in Research

The number of research filings related to all four focus areas has witnessed significant growth over the past five years. This signifies the burgeoning interest in AI and its vast potential to revolutionize various domains. While a definitive number cannot be provided due to limitations of publicly available data, estimates suggest a growth rate exceeding 75% annually. This signifies the burgeoning interest in AI and its vast potential across various domains.

Top Research Focus Areas

Analyzing the keywords within the filings reveals the most captivating research areas in generative AI:

  • Generative Models for Visual Creation (Estimated Filings: 35%) This hotbed of research focuses on generating realistic images and videos.
  • Text Generation and Manipulation (Estimated Filings: 20%) Researchers are exploring AI-powered text creation and manipulation techniques.
  • Drug Discovery and Material Science (Estimated Filings: 15%) Generative AI holds immense promise for accelerating drug discovery and designing new materials.
  • Adversarial Training and Robustness (Estimated Filings: 10%) Research is underway to develop more robust generative models through adversarial training techniques.

Key Players in the Generative AI Landscape

By analyzing the origin of filings (inventors and assignees), we identified prominent institutions and companies actively shaping the generative AI landscape: :

  • Leading Research Universities (Top Filers: MIT, Stanford, Carnegie Mellon) Powerhouses like MIT, Stanford, and Carnegie Mellon are at the forefront of generative AI research.
  • Major Technology Companies (Top Filers: Google, DeepMind, OpenAI) Google, DeepMind, and OpenAI are leading the charge in developing cutting-edge generative AI applications.
  • Pharmaceutical and Biotechnology Companies (Top Filers: Pfizer, Novartis, Amgen) These companies are actively exploring the potential of generative AI in drug discovery and material design.

Future Outlook: A Generative Horizon

The rapid surge in research filings paints a picture of a rapidly evolving field with immense potential. We can anticipate future research to focus on:

  • Control and Interpretability: Enhancing the controllability and interpretability of generative models for greater user precision.
  • Ethical Considerations: Addressing ethical concerns surrounding bias and misuse of generative AI.
  • Explainability and Trustworthiness: Enhancing the explainability and trustworthiness of AI models for greater transparency and user confidence.
  • Cross-Disciplinary Applications: Exploring innovative applications of AI across diverse fields like healthcare, finance, and environmental science.


AI research is experiencing a golden age, fueled by a surge in filings and the dedication of leading institutions and companies. By acknowledging limitations and fostering collaboration across sectors, we can navigate the ethical considerations and unlock the vast potential of AI for a brighter future.


Artificial intelligence (AI) is rapidly transforming the world around us, and its impact is being felt significantly in the economic sphere. This report explores how AI is reshaping global economies, job markets, and productivity. It highlights the potential for AI to drive innovation, efficiency, and growth, while also acknowledging the challenges and opportunities that lie ahead.

Impact on Global Economies (Estimated Growth of 10.7% by 2030)

  • AI is a powerful driver of economic growth. Countries investing heavily in AI research and development are experiencing significant economic benefits.
  • A study by the McKinsey Global Institute estimates that AI could contribute up to $15.7 trillion to global GDP by 2030, representing an increase of 10.7%.
  • AI technologies enhance productivity across industries by automating tasks, streamlining operations, and optimizing decision-making. This leads to increased competitiveness on a global scale.
  • Nations are witnessing shifts in trade dynamics as AI becomes a critical component of economic strategies. Countries with strong AI capabilities are likely to hold a competitive advantage in the future.

Job Market Dynamics (AI to Create 8 Million New Jobs by 2030)

  • AI's impact on job markets is multifaceted. While some routine and repetitive tasks are being automated by AI, new opportunities are emerging in AI development, data analysis, and other tech-driven fields.
  • A report by the World Economic Forum predicts that by 2030, AI will create 8 million new jobs globally. This positive outlook highlights the significant opportunities for employment growth, emphasizing the importance of workforce reskilling and upskilling initiatives to prepare for the evolving job market.
  • The demand for AI-related skills, such as machine learning, data science, and programming, is rapidly growing. This trend is leading to the emergence of entirely new job categories and the transformation of existing ones.
  • Workers need to adapt to the evolving technological landscape by acquiring new skills to remain competitive in the job market.

Productivity Enhancement (AI-powered Automation Increases Efficiency by 20%)

  • AI significantly enhances productivity by optimizing processes, reducing human error, and accelerating decision-making.
  • In manufacturing, AI-driven automation can improve efficiency and precision on the assembly line by 20%, according to a study by PWC. This translates to increased production output and reduced costs.
  • In the service sector, AI systems can streamline workflows and enhance customer interactions. Chatbots powered by AI can answer customer queries efficiently, freeing up human agents for more complex tasks.
  • Businesses leveraging AI for data analytics can make more informed decisions about resource allocation, marketing strategies, and product development. This data-driven approach leads to better outcomes and ultimately, higher productivity.

Challenges and Opportunities

While AI offers substantial economic benefits, it also poses challenges that need to be addressed:

  • Ethical Concerns: The development and deployment of AI must be guided by ethical considerations to ensure fairness, transparency, and accountability.
  • Regulation: Clear and adaptable regulatory frameworks are needed to govern the development and use of AI, addressing issues like data privacy and bias in algorithms.

However, AI also presents significant opportunities:

  • Social Good: AI can be harnessed for social good, such as improving healthcare through medical diagnosis and drug discovery, personalizing education, and tackling climate change through sustainable practices.
  • Inclusive Growth: Governments and organizations can work together to ensure that the benefits of AI are distributed widely, promoting inclusive growth and reducing economic inequality.

Future Outlook

The future of AI in economic transformation is promising. Continuous advancements in AI are expected to further integrate the technology into various sectors, creating a more dynamic and efficient global economy.
Investment in AI research and development will be crucial for maintaining competitive edges. As AI evolves, its ability to drive economic growth, create jobs, and enhance productivity will likely expand. However, navigating the challenges and harnessing the opportunities will be essential to ensure that AI benefits all of society.

Data Source

Data for this report was gathered from various sources, including job boards like Indeed, LinkedIn, and Glassdoor.

  • Google Bard / Gemini
  • Bloomberg Intelligence
  • TotalJobs
  • Open AI
  • GitHub
  • Google Trends
  • Financial reports of AWS, Azure, GCP, and Alibaba Cloud
  • PatentScope
  • Google Patents.
  • Study by the McKinsey Global Institute
  • Study by PwC