ai research ranking 2022

AI Institute and Author Rankings by Publications

• Core Areas: AIRankings includes the following six core areas of AI: Computer Vision, Natural Language, Machine Learning, Cognitive Reasoning, Robotics, and Multi-Agent Systems. In addition, we also take into account two areas: Simulation for training AI agents in graphics environments like VR and AR; and AI In General that encompasses a broad spectrum of fields in AI, which is not equivalent to the concept of artificial general intelligence. These eight areas play in synergy and have been integrated towards building general AI systems.
• Author Scores: Each author has two scores. Adjusted Publications is the author's total publications in selected areas, adjusted by two factors: an article is weighted by the importance of its venue, and an article with K senior co-authors (not counting students) will give 1/K score to each senior co-author. AI Index is the geometric average of adjusted publications for each selected area. It measures the breadth of an author, and gives a higher score to one with publications across multiple AI areas than another with the same number of publications focusing on a single area.
• Institute Scores: An institute's Adjusted Publications and AI Index are similar to those of an author, but including the publications of all its senior authors. When an author changes affiliations, his/her historical scores move with him/herself to the new institute. This design is based on the assumption that the level of research of an institute is determined by its current talents, rather than historical achievements.

AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

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VinAI recognised as one of the Top 20 global companies for ‘leading AI Research in 2022’ and publishes 88 top-tier publications

07/06/2022 / News

After just three years, VinAI marked its global prominence with 88 publications at the main research tracks of top-tier AI conferences. In addition, VinAI was ranked within a global top 20 of the ‘leading AI Research 2022’ by  Thundermark Capital .

The only Vietnamese company on the list, the ranking was awarded after an analysis of publications at the two most prestigious AI research conferences leading up to 2022: International Conference on Machine Learning (ICML 2021 –  https://icml.cc/ ) and Neural Information Processing Systems (NeurIPS 2021 –  https://nips.cc/ ).  

(source:  https://thundermark.medium.com/ai-research-rankings-2022-sputnik-moment-for-china-64b693386a4 )

Using conference proceedings,  Thundermark Capital  read 3,523 accepted papers (1,184 papers at ICML and 2,339 papers at NeurIPS) and compiled a list of authors and their affiliated organizations, and then calculated the Publication Index for each organization. The easiest and most intuitive way to understand the Publication Index is from the point of view of full paper equivalents: Google’s Publication Index of 200 can be interpreted as if Google published 200 full papers at the two leading AI conferences in 2021.

In another great achievement, the 88 published papers in three years are also a clear demonstration of VinAI’s innovation and dedication to world-class research.  VinAI had 14, 12, 11 and 11 research papers published at CVPR (h5-index 356), NeurIPS (h5-index 245), ICML (h5-index 204) and ICLR (h5-index 253), respectively. VinAI conducts world-class research in cutting-edge artificial intelligence (AI) technologies, with research spanning major pillars of AI from machine learning to computer vision and natural language processing.

The number of papers at each top-tier conference.

Over the last three years, VinAI has established Vietnam’s position on the global map of the AI research community with a remarkable publication record in the field. VinAI is the only company in Vietnam appearing in the ranking of companies in the top conferences, equally well-known companies such as Salesforce and Apple, etc. To highlight our achievements in the overall picture of AI research in Vietnam, our closest Vietnamese competitor only recently obtained its first publication accepted to a top conference. 

Focusing on Machine Learning, Computer Vision, and Natural Language Processing, VinAI research aims to address fundamental problems in these areas and develop practical methods that enable impactful applications. 

Along with conducting this pioneering research, another mission of VinAI is to nurture the next generation of Vietnamese AI scientists via a residency program. After two years of training with VinAI scientists, VinAI residents either acquire Ph.D. scholarships to study at the top 20 computer science universities in the world or become experienced AI engineers in Vietnam’s leading AI companies. In 2021 and 2022, residents attained 60 Ph.D. scholarships from the top universities such as  Carnegie Mellon University  (USA),  Harvard  (USA),  EPFL  (Switzerland),  Cornell  (USA), and more, making almost every resident who has applied for Ph.D. to get at least one admission.

Driven by a clear vision that delivers modern, impactful and comprehensive solutions, we seek out fresh, local talent and relish the ability to help guide the next generation of AI scientists/researchers to provide transformative technology solutions to the global market. With over 200 dedicated and competent personnel, of which almost 180 are AI scientists, SW engineers, embedded engineers, and AI residents, VinAI undertakes the mission of transforming our state-of-the-art AI research into impactful technology solutions for the global market.

VinAI is also dedicated to building and nurturing the AI Community within Vietnam during our annual VinAI Day. For the past three years, we have hosted this conference, with this year’s conference set to occur in Hanoi at the end of August.

VinAI  – formerly VinAI Research Institute belonging to  Vingroup JSC  – is one of the top 20 global AI companies developing world-class products and services. Our head office is located in Hanoi, Vietnam, with additional technology hubs across the US and Australia.​ Read our publications at  https://www.vinai.io/publications/

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AI 100: The most promising artificial intelligence startups of 2022

• May 17, 2022
• Artificial Intelligence
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The AI 100 is CB Insights' annual list of the 100 most promising private AI companies in the world. This year’s winners are working on diverse solutions designed to recycle plastic waste, improve hearing aids, combat toxic online gaming behavior, and more.

CB Insights has unveiled the winners of the sixth annual AI 100 — a list of the 100 most promising private AI companies across the globe.

Some of this year’s winners are advancing the development and use of artificial intelligence (AI) across specific industries — such as healthcare, gaming, and agriculture. On the other hand, some are developing applications to support sales, engineering design, cybersecurity, and other functions across a wide range of industries.

Additionally, a sizable portion of the companies in this cohort are developing tools, like machine learning (ML) platforms, to support AI development.

Using the CB Insights platform , our research team picked these 100 private market vendors from a pool of over 7K companies, including applicants and nominees. They were chosen based on factors including R&D activity, proprietary Mosaic scores , market potential, business relationships, investor profile, news sentiment analysis, competitive landscape, team strength, and tech novelty. The research team also reviewed thousands of Analyst Briefings submitted by applicants.

Clients can access the entire AI 100 list and interactive Expert Collection here . (If you don’t have a CB Insights login, create one here .)

Want to be considered for future rankings?   Fill out this initial application form  (it’ll take no more than a few minutes). If selected, you’ll be asked to complete our Analyst Briefing Survey so that our analysts can better understand your products, customers, and market traction.

Companies are categorized by their primary focus area and client base. Categories in the market map are not mutually exclusive.

Please click to enlarge.

Table of contents

• Top AI companies 2022: AI 100 cohort highlights
• The AI 100 Class of 2021: Where are they now? 

Top Ai companies 2022: AI 100 cohort highlights

We split this year’s cohort into 3 broad categories:

• AI development tools: Nearly one-third of the companies in this year’s cohort are working on solutions to support the management of various stages of the AI lifecycle, from data annotation to model training to model monitoring for algorithmic bias.
• Industry-specific applications: Forty-three of the winners are focused on applying AI to use cases specific to different industries, such as gaming, healthcare, and construction.
• Cross-industry applications: Vendors here are developing solutions that can be utilized across multiple industries, including warehouse & logistics robots, sales & contact center tech, and engineering design tools.

Below are a few highlights from the AI 100 Class of 2022.

Overall funding & valuation trends: The AI 100 includes a mix of companies at different stages of maturity, product development, and funding.

Overall, the cohort has raised $12B+ from 650 investors, across 300+ equity deals, since 2017 (as of 5/10/22). This year’s list includes 16 unicorns with a $1B+ valuation. (Note: Neither unicorn status nor the total amount of funding raised were included among the selection criteria for this year’s list.)

Global reach: This year’s winners represent 10 different countries across the globe. Seventy-three of the selected companies are headquartered in the US. The UK came in second with 8 winners, and Canada followed closely with 5.

Other countries home to a winner/winners on this year’s list include India, Sweden, China, and Germany. (Note: This geographical breakdown does not account for companies with multiple global headquarters.)

Early-stage innovation : Thirty-nine of our winners are seed/angel or Series A companies with promising product ideas.

To highlight a couple, GGWP , which was co-founded by former professional gamer Dennis Fong, is combating toxicity in online gaming. You.com has brought together a team of research scientists with experience at Salesforce and Stanford in order to develop a search engine that enables users to compare and sort results. 

Most-represented categories: Among the core industries highlighted on this map, healthcare holds the largest share of our winning cohort. The 10 companies featured in this category are focused on surgical tech ( ACTIV Surgical ), drug discovery for rare diseases ( Healx ), and more. Finance & insurance came in second with 7 winners, including vendors working on visual damage appraisal for cars ( Tractable ) and synthetic & anonymized financial datasets ( Hazy ).

Novel applications: A number of companies on this year’s list are working on niche applications where the use of AI is not commonplace yet.

To name a few, Whisper is developing sound separation tech to improve hearing aid performance. Notably, the company’s head of hardware engineering was previously a hardware engineer for Apple AirPods. Canvas Construction — founded by individuals with experience at Boston Dynamics and the MIT Lincoln Laboratory — is focused on AI-driven robotics for drywall finishing in the construction industry. Agility Robotics , on the other hand, is developing humanoid robots for warehouse and logistics use cases.

AI 100 (2022)

Track the 100 most promising private AI companies to watch in 2022.

the AI 100 class of 2021: where are they now?

The 2021 AI 100 winners have accomplished a great deal since April 2021. Together, they have seen:

• Over $6B in equity funding across 70+ deals
• 20 mega-rounds (deals worth $100M+), including a $600M round to an AI chip developer
• 9 exits, with some winners being acquired by tech leaders like Meta and Nvidia
• 6 new entrants to the $1B+ unicorn club
• A plethora of new partnerships with industry leaders like Geico, Cisco, and Snowflake

If you want to learn more about the AI 100 Class of 2021, check out the full list of previous winners .

• Mar 2, 2023

Must read: the 100 most cited AI papers in 2022

Updated: Mar 8, 2023

Who Is publishing the most Impactful AI research right now? With the breakneck pace of innovation in AI, it is crucial to pick up some signal as soon as possible. No one has the time to read everything, but these 100 papers are sure to bend the road as to where our AI technology is going. The real test of impact of R&D teams is of course how the technology appears in products, and OpenAI shook the world by releasing ChatGPT at the end of November 2022, following fast on their March 2022 paper “Training language models to follow instructions with human feedback”. Such fast product adoption is rare, so to see a bit further, we look at a classic academic metric: the number of citations. A detailed analysis of the 100 most cited papers per year, for 2022, 2021, and 2020 allows us to draw some early conclusions. The United States and Google still dominate, and DeepMind has had a stellar year of success, but given its volume of output, OpenAI is really in a league of its own both in product impact, and in research that becomes quickly and broadly cited. The full top-100 list for 2022 is included below in this post.

Using data from the Zeta Alpha platform combined with careful human curation (more about methodology below), we've gathered the top cited papers in AI from 2022, 2021, and 2020, and analyzed authors' affiliations, and country. This allows us to rank these by R&D impact rather than pure publication volume.

What are some of these top papers we're talking about?

But before we dive into the numbers, let's get a sense of what papers we're talking about: the blockbusters from these past 3 years. You'll probably recognize a few of them!

1️⃣ AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models -> (From DeepMind, 1372 citations) Using AlphaFold to augment protein structure database coverage.

2️⃣ ColabFold: making protein folding accessible to all -> (From multiple institutions, 1162 citations) An open-source and efficient protein folding model.

3️⃣ Hierarchical Text-Conditional Image Generation with CLIP Latents -> (From OpenAI, 718 citations) DALL·E 2, complex prompted image generation that left most in awe.

4️⃣ A ConvNet for the 2020s -> (From Meta and UC Berkeley, 690 citations) A successful modernization of CNNs at a time of boom for Transformers in Computer Vision.

5️⃣ PaLM: Scaling Language Modeling with Pathways -> (From Google, 452 citations) Google's mammoth 540B Large Language Model, a new MLOps infrastructure, and how it performs.

1️⃣ Highly accurate protein structure prediction with AlphaFold -> (From DeepMind, 8965) AlphaFold, a breakthrough in protein structure prediction using Deep Learning. See also " Accurate prediction of protein structures and interactions using a three-track neural network " (from multiple academic institutions, 1659 citations), an open-source protein structure prediction algorithm.

2️⃣ Swin Transformer: Hierarchical Vision Transformer using Shifted Windows -> (From Microsoft, 4810 citations) A robust variant of Transformers for Vision.

3️⃣ Learning Transferable Visual Models From Natural Language Supervision -> (From OpenAI, 3204 citations) CLIP, image-text pairs at scale to learn joint image-text representations in a self-supervised fashion

4️⃣ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? -> (From U. Washington, Black in AI, The Aether, 1266 citations) Famous position paper is very critical of the trend of ever-growing language models, highlighting their limitations and dangers.

5️⃣ Emerging Properties in Self-Supervised Vision Transformers -> (From Meta, 1219 citations) DINO, showing how self-supervision on images led to the emergence of some sort of proto-object segmentation in Transformers.

1️⃣ An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale -> (From Google, 11914 citations) The first work showing how a plain Transformer could do great in Computer Vision.

2️⃣ Language Models are Few-Shot Learners -> (From OpenAI, 8070 citations) GPT-3, This paper does not need further explanation at this stage.

3️⃣ YOLOv4: Optimal Speed and Accuracy of Object Detection -> (From Academia Sinica, Taiwan, 8014 citations) Robust and fast object detection sells like hotcakes.

4️⃣ Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer -> (From Google, 5906 citations) A rigorous study of transfer learning with Transformers, resulting in the famous T5.

5️⃣ Bootstrap your own latent: A new approach to self-supervised Learning -> (From DeepMind and Imperial College, 2873 citations) Showing that negatives are not even necessary for representation learning.

Read on below to see the full list of 100 papers for 2022, but let's first dive into the analyses for countries and institutions.

The most cited papers from the past 3 years

When we look at where these top-cited papers come from (Figure 1), we see that the United States continues to dominate and the difference among the major powers varies only slightly per year. Earlier reports that China may have overtaken the US in AI R&D seem to be highly exaggerated if we look at it from the perspective of citations. We also see an impact significantly above expectation from Singapore and Australia.

To properly assess the US dominance, let's look beyond paper count numbers. If we consider the accumulated citations by country instead, the difference looks even stronger. We have normalized by the total number of citations in a year, in order to be able to compare meaningfully across years.

Figure 2. Source: Zeta Alpha

The UK is clearly the strongest player outside of the US and China. However, the contribution of the UK is even more strongly dominated by DeepMind in 2022 (69% of the UK total), than in the previous years (60%). DeepMind has truly had a very productive 2022. Looking at the regions, North America is leading by a large margin while Asia is slightly above Europe.

Figure 3. Source: Zeta Alpha

Now let's look at how the leading organizations compare by number of papers in the top 100.

Figure 4. Source: Zeta Alpha

Google is consistently the strongest player followed by Meta, Microsoft, UC Berkeley, DeepMind and Stanford. While industry calls the shots in AI research these days, and single academic institutions don't produce as much impact, the tail for these institutions is much longer, so that when we aggregate by organization type, it evens out.

Figure 5. Source: Zeta Alpha

If we look into total research output, how many papers have organizations published in these past 3 years?

Figure 6. Source: Zeta Alpha

In total publication volume, Google is still in the lead, but differences are much less drastic compared to the citation top 100. You won't see OpenAI or DeepMind among the top 20 in the volume of publications. These institutions publish less but with higher impact. The following chart shows the rate at which organizations manage to convert their publications into top-100 papers.

Now we see that OpenAI is simply in a league of its own when it comes to turning publications into absolute blockbusters. While certainly, their marketing magic helps a lot to propel their popularity, it's undeniable that some of their recent research is of outstanding quality. With a lower paper volume but impressive conversion rate is also EleutherAI, the non-profit collective focusing on interpretability and alignment of large Language Models.

The top 100 most cited papers for 2022

And finally, here is our top-100 list itself, with titles, citation counts, and affiliations.

We have also added twitter mentions, which are sometimes seen as an early impact indicator, however the correlation so far seems to be weak. Further work is needed. Here you have the list for the year 2020 and for 2021 (as tsv files).

Methodology

To create the analysis above, we have first collected the most cited papers per year in the Zeta Alpha platform , and then manually checked the first publication date (usually an arXiv pre-print), so that we place papers in the right year. We supplemented this list by mining for highly cited AI papers on Semantic Scholar with its broader coverage and ability to sort by citation count. This mainly turns up additional papers from highly impactful closed-source publishers (e.g. Nature, Elsevier, Springer and other journals). We then take for each paper the number of citations on Google Scholar as the representative metric and sort the papers by this number to yield the top-100 for a year. For these papers we used GPT-3 to extract the authors, their affiliations, and their country and manually checked these results (if the country was not clearly visible from the publication, we take the country of the organization’s headquarters). A paper with authors from multiple affiliations counts once for each of the affiliations.

Updates 2023/03/07

- Update the 2022 list with the following papers:

- Emergent Abilities of Large Language Models (74 citations)

- Self-consistency improves chain of thought reasoning in language models (71 citations)

- Why do tree-based models still outperform deep learning on tabular data? (60 citations)

- DeiT III: Revenge of the ViT (44 citations)

- Fix missing EleutherAI as the 2nd best organization in terms of conversion rate

- Added links to the full 2020 top-cited paper list and 2021 top-cited paper lists

- Add counts by region plot

- Fix missing countries and organizations in the 2022 list

This concludes our analysis; what surprised you the most about these numbers? Try out our platform , follow us on Twitter @zetavector and let us know if you have any feedback or would like to receive a more detailed analysis for your domain or organization.

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Trends in AI — June 2024

Trends in AI — May 2024

Trends in AI — April 2024

The state of AI in 2022—and a half decade in review

You have reached a page with older survey data. please see our 2024 survey results here ..

Adoption has more than doubled since 2017, though the proportion of organizations using AI 1 In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). has plateaued between 50 and 60 percent for the past few years. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development , and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling. The data show that there is significant room to improve diversity on AI teams, and, consistent with other studies, diverse teams correlate with outstanding performance.

Table of Contents

• Five years in review: AI adoption, impact, and spend
• Mind the gap: AI leaders pulling ahead
• AI talent tales: New hot roles, continued diversity woes

About the research

1. five years in review: ai adoption, impact, and spend.

This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period.

2. Mind the gap: AI leaders pulling ahead

Over the past five years we have tracked the leaders in AI—we refer to them as AI high performers—and examined what they do differently. We see more indications that these leaders are expanding their competitive advantage than we find evidence that others are catching up.

First, we haven’t seen an expansion in the size of the leader group. For the past three years, we have defined AI high performers as those organizations that respondents say are seeing the biggest bottom-line impact from AI adoption—that is, 20 percent or more of EBIT from AI use. The proportion of respondents falling into that group has remained steady at about 8 percent. The findings indicate that this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing costs as well.

Next, high performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes  (Exhibit 1). 2 All questions about AI-related strengths and practices were asked only of the 744 respondents who said their organizations had adopted AI in at least one function, n = 744. Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale , or what some call the “ industrialization of AI .” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly. They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs , which allow companies to speed up the creation of AI applications. In the past year, high performers have become even more likely than other organizations to follow certain advanced scaling practices, such as using standardized tool sets to create production-ready data pipelines and using an end-to-end platform for AI-related data science, data engineering, and application development that they’ve developed in-house.

High performers might also have a head start on managing potential AI-related risks, such as personal privacy and equity and fairness, that other organizations have not addressed yet. While overall, we have seen little change in organizations reporting recognition and mitigation of AI-related risks since we began asking about them four years ago, respondents from AI high performers are more likely than others to report that they engage in practices that are known to help mitigate risk . These include ensuring AI and data governance , standardizing processes and protocols , automating processes such as data quality control to remove errors introduced through manual work, and testing the validity of models and monitoring them over time for potential issues.

AI use and sustainability efforts

The survey findings suggest that many organizations that have adopted AI are integrating AI capabilities into their sustainability efforts and are also actively seeking ways to reduce the environmental impact of their AI use (exhibit). Of respondents from organizations that have adopted AI, 43 percent say their organizations are using AI to assist in sustainability efforts, and 40 percent say their organizations are working to reduce the environmental impact of their AI use by minimizing the energy used to train and run AI models. As companies that have invested more in AI and have more mature AI efforts than others, high performers are 1.4 times more likely than others to report AI-enabled sustainability efforts as well as to say their organizations are working to decrease AI-related emissions. Both efforts are more commonly seen at organizations based in Greater China, Asia–Pacific, and developing markets, while respondents in North America are least likely to report them.

When asked about the types of sustainability efforts using AI, respondents most often mention initiatives to improve environmental impact, such as optimization of energy efficiency or waste reduction. AI use is least common in efforts to improve organizations’ social impact (for example, sourcing of ethically made products), though respondents working for North American organizations are more likely than their peers to report that use.

Investment is yet another area that could contribute to the widening of the gap: AI high performers are poised to continue outspending other organizations on AI efforts. Even though respondents at those leading organizations are just as likely as others to say they’ll increase investments in the future, they’re spending more than others now, meaning they’ll be increasing from a base that is a higher percentage of revenues. Respondents at AI high performers are nearly eight times more likely than their peers to say their organizations spend at least 20 percent of their digital-technology budgets on AI-related technologies. And these digital budgets make up a much larger proportion of their enterprise spend: respondents at AI high performers are over five times more likely than other respondents to report that their organizations spend more than 20 percent of their enterprise-wide revenue on digital technologies.

Finally, all of this may be giving AI high performers a leg up in attracting AI talent. There are indications that these organizations have less difficulty hiring for roles such as AI data scientist and data engineer. Respondents from organizations that are not AI high performers say filling those roles has been “very difficult” much more often than respondents from AI high performers do.

The bottom line: high performers are already well positioned for sustained AI success, improved efficiency in new AI development, and a resultingly more attractive environment for talent. The good news for organizations outside the leader group is that there’s a clear blueprint of best practices for success.

3. AI talent tales: New hot roles, continued diversity woes

Our first detailed look at the AI talent picture signals the maturation of AI, surfaces the most common strategies organizations employ for talent sourcing and upskilling, and shines a light on AI’s diversity problem—while showing yet again a link between diversity and success.

Hiring is a challenge, but less so for high performers

All organizations report that hiring AI talent, particularly data scientists, remains difficult. AI high performers report slightly less difficulty and hired some roles, like machine learning engineers, more often than other organizations.

Reskilling and upskilling are common alternatives to hiring

When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half are doing so. Recruiting from top-tier universities as well as from technology companies that aren’t in the top tier, such as regional leaders, are also common strategies. But a look at the strategies of high performers suggests organizations might be best served by tapping as many recruiting channels as possible (Exhibit 2). These companies are doing more than others to recruit AI-related talent from various sources. The findings show that while they’re more likely to recruit from top-tier technical universities and tech companies, they’re also more likely to source talent from other universities, training academies, and diversity-focused programs or professional organizations.

Responses suggest that both AI high performers and other organizations are upskilling technical and nontechnical employees on AI, with nearly half of respondents at both AI high performers and other organizations saying they are reskilling as a way of gaining more AI talent. However, high performers are taking more steps than other organizations to build employees’ AI-related skills.

Respondents at high performers are nearly three times more likely than other respondents to say their organizations have capability-building programs to develop technology personnel’s AI skills. The most common approaches they use are experiential learning , self-directed online courses, and certification programs, whereas other organizations most often lean on self-directed online courses.

High performers are also much more likely than other organizations to go beyond providing access to self-directed online course work to upskill nontechnical employees on AI. Respondents at high performers are nearly twice as likely as others to report offering peer-to-peer learning and certification programs to nontechnical personnel.

Increasing diversity on AI teams is a work in progress

We also explored the level of diversity within organizations’ AI-focused teams, and we see that there is significant room for improvement at most organizations. The average share of employees on these teams at respondents’ organizations who identify as women is just 27 percent (Exhibit 3). The share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25 percent. What’s more, 29 percent of respondents say their organizations have no minority employees working on their AI solutions.

Some companies are working to improve the diversity of their AI talent, though there’s more being done to improve gender diversity than ethnic diversity. Forty-six percent of respondents say their organizations have active programs to increase gender diversity within the teams that are developing AI solutions, through steps such as partnering with diversity-focused professional associations to recruit candidates. One-third say their organizations have programs to increase racial and ethnic diversity. We also see that organizations with women or minorities working on AI solutions often have programs in place to address these employees’ experiences.

In line with previous McKinsey studies , the research shows a correlation between diversity and outperformance. Organizations at which respondents say at least 25 percent of AI development employees identify as women are 3.2 times more likely than others to be AI high performers. Those at which at least one-quarter of AI development employees are racial or ethnic minorities are more than twice as likely to be AI high performers.

The online survey was in the field from May 3 to May 27, 2022, and from August 15 to August 17, 2022, and garnered responses from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 744 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office; Bryce Hall , an associate partner in the Washington, DC, office; Helen Mayhew , a partner in the Sydney office; and Alex Singla , a senior partner in the Chicago office, and Alex Sukharevsky , a senior partner in the London office, global leaders of QuantumBlack, AI by McKinsey.

The authors wish to thank Sanath Angalakudati, Medha Bankhwal, David DeLallo, Heather Hanselman, Vishan Patel, and Wilbur Wang for their contributions to this work.

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Intelligence | Global AI Index

The Global AI Index

The first index to benchmark nations on their level of investment, innovation and implementation of artificial intelligence.

Making sense of artificial intelligence… on a global scale

The artificial intelligence revolution will transform business, government and society – and this year it’s taken a huge leap forward. The rise of ChatGPT and the ensuing arms race between big tech companies to develop their own generative AI models has led to a very public debate about how best to manage the risks of this new technology. There’s been a lot of talk, but little understanding.

The Global AI Index aims to make sense of artificial intelligence in 62 countries that have chosen to invest in it. It’s the first ever ranking of countries based on three pillars of analysis; investment, innovation and implementation. This is the fourth iteration of the index.

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Ranking Table

Countries are ranked by their AI capacity at the international level. This is the fourth iteration of the Global AI Index, published on 28 June 2023.

The Global AI Index is underpinned by 111 indicators, collected from 28 different public and private data sources, and 62 governments. These are split across seven sub-pillars: Talent, Infrastructure, Operating Environment, Research, Development, Government Strategy and Commercial.

Implementation

Talent  focuses on the availability of skilled practitioners in artificial intelligence solutions. Infrastructure  assesses the reliability and scale of access infrastructure, from electricity and internet to supercomputing capabilities.

Operating Environment focuses on the regulatory context and public opinion on artificial intelligence.

Innovation 

Research looks at the extent of specialist research and researchers, including numbers of publications and citations in credible academic journals.

Development focuses on the development of fundamental platforms and algorithms upon which innovative artificial intelligence projects rely.

Government Strategy gauges the depth of commitment from national governments to artificial intelligence; investigating spending commitments and national strategies.

Commercial focuses on the level of startup activity, investment and business initiatives based on artificial intelligence.

Country Profile s

Overall rank positions for each sub-pillar of the Index

Explore the 100+ Indicators that make up the Global AI Index

Further Reading

See our Results Piece: read here

And our Index Methodology: read here

Serena Cesareo and Joseph White

If you’d like to get in touch about the Global AI Index, please email [email protected]

Top 10 artificial intelligence companies in 2022

10 - Cloudera

Cloudera , a hybrid cloud data company, supplies a cloud platform for analytics and machine learning built by people from leading companies like Google, Yahoo!, Facebook and Oracle. The technology gives companies a comprehensive view of their data in one place, providing clearer insights and better protection. Cloudera’s data services are modular practitioner-focused analytic capabilities, providing a consistent experience in any cloud. They can be standalone offerings or integrated into solutions that deliver a seamless data lifecycle experience.

9 - Salesforce

Salesforce develops artificial intelligence for customer relationship management (CRM). This is delivered as an embedded layer of intelligence within the Salesforce platform, powered by its Einstein AI engine. Founded in 1999, the company has become almost synonymous with CRM and leverages AI for nearly every task. It launched Salesforce Einstein in 2016 as “artificial intelligence for everyone” and continues to build the technology while keeping in mind ethical considerations. In 2021, Salesforce debuted its AI Ethics model. 

8 - SenseTime

SenseTime builds AI technologies for business operations, smart cities, smart homes, and smart cars. The company was founded by Tang Xiao and Xu Li in 2014, who were both researchers at that time. They developed multiple white papers outlining the future possibilities of AI technology and eventually partnered with Qualcomm in 2017. The company is one of the founding members of the Global Artificial Intelligence Academic Alliance (GAIAA).

7 - Dataiku

Dataiku is the platform for Everyday AI that allows companies to leverage one central solution to design, deploy, govern, and manage AI and analytics applications. The company has a global team of over 1000 people to bring the vast potential of AI to over 450 companies worldwide. In August, Dataiku raised US$400mn at a US$4.6bn valuation to enable Everyday AI in the enterprise. 

Companies worldwide use Dataiku to handle increasingly complex use cases to find increasing value in their data and strengthen relationships with strategic global partners by driving technical enablement, product adoption, and AI maturity. 

Founded in 2011, H2O.ai has grown to become one of the leading artificial intelligence (AI) cloud companies and is now recognised as a global visionary and thought leader in automated machine learning (autoML), time series forecasting and responsible AI. 

The world’s leading data scientists and engineers come to work at H2O.ai to deliver on its vision of making AI accessible to any business, government entity, non-profit or academic institution in the world. Launched in 2021, the H2O AI Cloud offers customers a single, unified platform architected from the ground up to support our core mission of democratising AI. 

5 - Metaverse Platforms

Meta Platforms develops artificial intelligence for immersive technologies and social media. It has several AI initiatives in the works, from AI for naturalised interactions in virtual reality environments to AI detectors for harmful content.  Founded in 2004, the company has a long history of working with AI tools, using the technology to match people, products, and content. In addition to its existing capabilities, Meta recently announced an AI supercomputer that can work across hundreds of languages, develop augmented reality tools, and pave the way towards the metaverse. 

4 - DeepMind

DeepMind is an AI research and development company that operates as a subsidiary of Alphabet. It also develops AI for positive outcomes in the healthcare sector. British academics Demis Hassabis, Shane Legg, and Mustafa Suleyman founded the company in 2010. The startup trained its AI algorithms on old games from the ‘70s and ‘80s to make it incrementally more intelligent over time. Google acquired DeepMind for $500 million in 2014. 

C3.ai is the world’s leading provider of Enterprise AI. Founded in 2009, the company aims to support and accelerate digital transformation with its proven C3 AI Suite, an end-to-end platform for developing, deploying and operating large-scale AI applications.

The C3 AI Suite provides comprehensive services to build enterprise-scale AI applications more efficiently and cost-effectively than alternative approaches. It also supports the value chain in any industry with prebuilt, configurable, high-value AI applications for reliability, fraud detection, sensor network health, supply network optimisation, energy management, anti-money laundering, and customer engagement.

IBM has been a leader in the field of artificial intelligence since the 1950s. The company ’s core offering is IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. IBM’s portfolio of business-ready tools, applications and solutions, are designed to reduce the costs and hurdles of AI adoption while optimising outcomes and responsible use of AI. 70% of global banking institutions use Watson and 13 of the top 14 systems integrators use Watson.

1 - Amazon Web Services

A leader in cloud computing, Amazon Web Services (AWS) offers both consumer and business-oriented AI products and services, and many of its professional AI services build on the AI services available in consumer products. 

Its Amazon Echo brings AI into the home with Alexa. For AWS , the company’s primary AI services include Lex, a business version of Alexa; Polly, which turns text into speech; and Rekognition, an image recognition service. The company also conducts an AI innovation contest with prizes up to $500,000.

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• Event calendar

The Top 17 ‘Must-Read’ AI Papers in 2022

We caught up with experts in the RE•WORK community to find out what the top 17 AI papers are for 2022 so far that you can add to your Summer must reads. The papers cover a wide range of topics including AI in social media and how AI can benefit humanity and are free to access.

Interested in learning more? Check out all the upcoming RE•WORK events to find out about the latest trends and industry updates in AI here .

Max Li, Staff Data Scientist – Tech Lead at Wish

Max is a Staff Data Scientist at Wish where he focuses on experimentation (A/B testing) and machine learning.  His passion is to empower data-driven decision-making through the rigorous use of data. View Max’s presentation, ‘Assign Experiment Variants at Scale in A/B Tests’, from our Deep Learning Summit in February 2022 here .

1. Boostrapped Meta-Learning (2022) – Sebastian Flennerhag et al.

The first paper selected by Max proposes an algorithm in which allows the meta-learner teach itself, allowing to overcome the meta-optimisation challenge. The algorithm focuses meta-learning with gradients, which guarantees improvements in performance. The paper also looks at how bootstrapping opens up possibilities. Read the full paper here .

2. Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces (2022) – Samuel Daulton et al.

Another paper selected by Max proposes MORBO, a scalable method for multiple-objective BO as it performs better than that of high-dimensional search spaces. MORBO significantly improves the sample efficiency, and where BO algorithms fail, MORBO provides improved sample efficiencies to the current BO approach used. Read the full paper here .

3. Tabular Data: Deep Learning is Not All You Need (2021) – Ravid Shwartz-Ziv, Amitai Armon

To solve real-life data science problems, selecting the right model to use is crucial. This final paper selected by Max explores whether deep models should be recommended as an option for tabular data. Read the full paper here .

Jigyasa Grover, Senior Machine Learning Engineer at Twitter

Jigyasa Grover is a Senior Machine Learning Engineer at Twitter working in the performance ads ranking domain. Recently, she was honoured with the 'Outstanding in AI: Young Role Model Award' by Women in AI across North America. She is one of the few ML Google Developer Experts globally. Jigyasa has previously presented at our Deep Learning Summit and MLOps event in San Fransisco earlier this year.

4. Privacy for Free: How does Dataset Condensation Help Privacy? (2022) – Tian Dong et al.

Jigyasa’s first recommendation concentrates on Privacy Preserving Machine Learning, specifically mitigating the leakage of sensitive data in Machine Learning. The paper provides one of the first propositions of using dataset condensation techniques to preserve the data efficiency during model training and furnish membership privacy. This paper was published by Sony AI and won the Outstanding Paper Award at ICML 2022. Read the full paper here .

5. Affective Signals in a Social Media Recommender System (2022) – Jane Dwivedi-Yu et al.

The second paper recommended by Jigyasa talks about operationalising Affective Computing, also known as Emotional AI, for an improved personalised feed on social media. The paper discusses the design of an affective taxonomy customised to user needs on social media. It further lays out the curation of suitable training data by combining engagement data and data from a human-labelling task to enable the identification of the affective response a user might exhibit for a particular post. Read the full paper here .

6. ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest (2022) – Paul Baltescu et al.

Jigyasa’s last recommendation is a paper by Pinterest that illustrates the aggregation of both textual and visual information to build a unified set of product embeddings to enhance recommendation results on e-commerce websites. By applying multi-task learning, the proposed embeddings can optimise for multiple engagement types and ensures that the shopping recommendation stack is efficient with respect to all objectives. Read the full article here .

Asmita Poddar, Software Development Engineer at Amazon Alexa

Asmita is a Software Development Engineer at Amazon Alexa, where she works on developing and productionising natural language processing and speech models. Asmita also has prior experience in applying machine learning in diverse domains. Asmita will be presenting at our London AI Summit , in September, where she will discuss AI for Spoken Communication.

7. Competition-Level Code Generation with AlphaCode (2022) – Yujia Li et al.

Systems can help programmers become more productive. Asmita has selected this paper which addresses the problems with incorporating innovations in AI into these systems. AlphaCode is a system that creates solutions for problems that requires deeper reasoning. Read the full paper here .

8. A Commonsense Knowledge Enhanced Network with Retrospective Loss for Emotion Recognition in Spoken Dialog (2022) – Yunhe Xie et al.

There are limits to model’s reasoning in regards to the existing ERSD datasets. The final paper selected by Asmita proposes a Commonsense Knowledge Enhanced Network with a backward-looking loss to perform dialog modelling, external knowledge integration and historical state retrospect. The model used has been shown to outperform other models. Read the full paper here .

Discover the speakers we have lined up and the topics we will cover at the London AI Summit.

Sergei Bobrovskyi, Expert in Anomaly Detection for Root Cause Analysis at Airbus

Dr. Sergei Bobrovskyi is a Data Scientist within the Analytics Accelerator team of the Airbus Digital Transformation Office. His work focuses on applications of AI for anomaly detection in time series, spanning various use-cases across Airbus. Sergei will be presenting at our Berlin AI Summit in October about Anomaly Detection, Root Cause Analysis and Explainability.

9. LaMDA: Language Models for Dialog Applications (2022) – Romal Thoppilan et al.

The paper chosen by Sergei describes the LaMDA system, which caused the furor this summer, when a former Google engineer claimed it has shown signs of being sentient. LaMDA is a family of large language models for dialog applications based on Transformer architecture. The interesting feature of the model is their fine-tuning with human annotated data and possibility to consult external sources. In any case, this is a very interesting model family, which we might encounter in many of the applications we use daily. Read the full paper here .

10. A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 (2022) – Yann LeCun

The second paper chosen by Sergei provides a vision on how to progress towards general AI. The study combines a number of concepts including configurable predictive world model, behaviour driven through intrinsic motivation, and hierarchical joint embedding architectures. Read the full paper here .

11. Coordination Among Neural Modules Through a Shared Global Workpace (2022) – Anirudh Goyal et al.

This paper chosen by Sergei combines the Transformer architecture underlying most of the recent successes of deep learning with ideas from the Global Workspace Theory from cognitive sciences. This is an interesting read to broaden the understanding of why certain model architectures perform well and in which direction we might go in the future to further improve performance on challenging tasks. Read the full paper here .

12. Magnetic control of tokamak plasmas through deep reinforcement learning (2022) – Jonas Degrave et al.

Sergei chose the next paper, which asks the question of ‘how can AI research benefit humanity?’. The use of AI to enable safe, reliable and scalable deployment of fusion energy could contribute to the solution of pression problems of climate change. Sergei has said that this is an extremely interesting application of AI technology for engineering. Read the full paper here .

13. TranAd: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022) – Shreshth Tuli, Giuliano Casale and Nicholas R. Jennings

The final paper chosen by Sergei is a specialised paper applying transformer architecture to the problem of unsupervised anomaly detection in multivariate time-series. Many architectures which were successful in other fields are at some points being also applied to time-series. The paper shows an improved performance on some known data sets. Read the full paper here .

Abdullahi Adamu, Senior Software Engineer at Sony

Abdullahi has worked in various industries including working at a market research start-up where he developed models that could extract insights from human conversations about products or services. He moved to Publicis, where he became Data Engineer and Data Scientist in 2018. Abdullahi will be part of our panel discussion at the London AI Summit in September, where he will discuss Harnessing the Power of Deep Learning.

14. Self-Supervision for Learning from the Bottom Up (2022) – Alexei Efros

This paper chosen by Abdullahi makes compelling arguments for why self-supervision is the next step in the evolution of AI/ML for building more robust models. Overall, these compelling arguments justify even further why self-supervised learning is important on our journey towards more robust models that generalise better in the wild. Read the full paper here .

15. Neural Architecture Search Survey: A Hardware Perspective (2022) – Krishna Teja Chitty-Venkata and Arun K. Somani

Another paper chosen by Abdullahi understands that as we move towards edge computing and federated learning, neural architecture search that takes into account hardware constraints which will be more critical in ensuring that we have leaner neural network models that balance latency and generalisation performance. This survey gives a birds eye view of the various neural architecture search algorithms that take into account hardware constraints to design artificial neural networks that give the best tradeoff of performance and accuracy. Read the full paper here .

16. What Should Not Be Contrastive In Contrastive Learning (2021) – Tete Xiao et al.

In the paper chosen by Abdullahi highlights the underlying assumptions behind data augmentation methods and how these can be counter productive in the context of contrastive learning; for example colour augmentation whilst a downstream task is meant to differentiate colours of objects. The result reported show promising results in the wild. Overall, it presents an elegant solution to using data augmentation for contrastive learning. Read the full paper here .

17. Why do tree-based models still outperform deep learning on tabular data? (2022) – Leo Grinsztajn, Edouard Oyallon and Gael Varoquaux

The final paper selected by Abdulliah works on answering the question of why deep learning models still find it hard to compete on tabular data compared to tree-based models. It is shown that MLP-like architectures are more sensitive to uninformative features in data, compared to their tree-based counterparts. Read the full paper here .

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Join us at our upcoming events this year:

·       London AI Summit – 14-15 September 2022

·       Berlin AI Summit – 4-5 October 2022

·       AI in Healthcare Summit Boston – 13-14 October 2022

·       Sydney Deep Learning and Enterprise AI Summits – 17-18 October 2022

·       MLOps Summit – 9-10 November 2022

·       Toronto AI Summit – 9-10 November 2022

·       Nordics AI Summit - 7-8 December 2022

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• Main 20 AI countries 2023, by research capacity

The United States had the strongest capacity for research among the leading 20 AI nations worldwide in 2023. It has a ranking of 100, compared with its nearest competitor China at just around 54. In general, Europe has the strongest showing, with most nations on the ranking hailing from Europe.

Computer science still male-driven

Research requires educated and skilled individuals to do it and in the modern world, computer science education is still male-dominated. Both in Europe and the United States, it is males that trend far more towards bachelor’s degrees in computer science. However, females had a higher trend toward masters in this field in Europe , whereas, in the United States, their share was similar to their share of bachelor's degrees .

U.S. tech giants play a significant role

One primary driver of the United States' significant lead over other nations is the fact that it houses the largest tech companies on earth. Counting giants such as Microsoft, Google, Apple, and Amazon, the country has ample funds in private hands to drive AI research even without the enormous budget of the U.S. government . 

Leading 20 artificial intelligence (AI) countries in 2023, by research capacity

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Further Content: You might find this interesting as well

Government AI Readiness Index 2023

The 2023 edition is here! Oxford Insights remains committed to providing valuable insights at the intersection of government and AI. This year we assess the AI readiness of 193 governments across the world. We are also introducing an interactive map to make our data more accessible! 

Executive summary

In 2023, artificial intelligence (AI) was in the headlines more than ever. Generative AI breakthroughs, major developments in the field of AI regulation like the European Union’s AI Act, and a significant increase in AI-related summits globally have put this technology in the spotlight. The transformative potential of AI is undeniable, with governments worldwide acknowledging its impact.  

Governments are not only working to foster AI innovation and establish regulatory frameworks but also striving to integrate this technology into public services. However, understanding how to ensure that AI is adopted effectively for the public good remains a challenge. This index attempts to address this issue. Our primary research question remains unchanged: how ready is a given government to implement AI in the delivery of public services to their citizens?  

We include 39 indicators across 10 dimensions, which make up 3 pillars: Government, Technology Sector, and Data & Infrastructure . This year, we rank 193 countries, up from 181 in last year’s iteration. 

Introducing our interactive map

Our Government AI Readiness Index aims to provide valuable insights for effective and responsible AI integration into public services. A key part of this mission is ensuring our data is user-friendly. This year, we are introducing an interactive map enabling users to compare data across countries, regions, and income groups . Scroll down to analyse the data yourself!

Our Findings

Government pillar, the number of ai strategies released per year has decreased, but the picture looks more diverse..

Global AI strategy releases have decreased overall, attributed to fewer strategies from higher-income countries. However, this year sees a marked shift with half of the launched or announced strategies coming from low and lower middle income nations. Notably, Rwanda became the first low-income country to publish an AI strategy.

Technology Sector Pillar

There is a disparity between high income countries and everyone else, though some large middle income economies punch above their weight..

High income countries score much higher than countries in any other income group in the Technology Sector pillar, with the gap between high income and upper middle income countries larger than the gaps between all other income groups combined in some cases. However, large middle income countries like Malaysia and the BRIC countries outperform their income groups and rank among the top 50 countries worldwide in this pillar. 

Data & Infrastructure Pillar

The digital divide remains a global challenge..

The Data and Infrastructure pillar underscores a significant digital divide, both between income groups and regions. While generative AI holds promise for lower-income countries, lacking a solid foundation in data and infrastructure may lead to reliance on foreign technology, introducing hurdles like language disparities and biases. Addressing these challenges is essential for fostering equitable and inclusive progress in AI readiness globally.

Global Governance Trends

2023 saw increased international collaboration on ai, especially on ai governance and ethics..

In 2023, global collaboration on AI governance surged, marked by increased international AI summits and the release of proposed frameworks such as the G7’s International Guiding Principles. Regional collaboration also expanded, as seen in agreements like the Santiago Declaration in Latin America. Notably, AI is now integral to the international development agenda, with countries like Rwanda and Senegal publishing national AI strategies with the support of cooperation agencies.

These summaries are just a glimpse into our global findings.

Download the full report for detailed global analysis, regional reports, a summary of our methodology, and the complete rankings.

2023 Index Rankings

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Regional Analysis

• North America
• Western Europe
• Eastern Europe
• Middle East and North Africa
• Latin America and the Caribbean
• South and Central Asia
• Sub-Saharan Africa

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Top 10 AI Companies in 2022

Today, 50% of companies use artificial intelligence in at least one business function, increasing the demand for AI solutions from the world’s top technology vendors.

Valued at $62.35 billion in 2020, the global artificial intelligence (AI) market will grow by over 40% in the next six years. AI and its component technologies like machine learning, natural language processing, and object and voice recognition can solve the vast majority of business problems with an incredible degree of efficiency and accuracy. This article evaluates the top 10 companies developing AI technology in 2022 and why they stand out in this competitive space. 

Table of Contents

• Comparison of Top AI Companies in 2022

Artificial intelligence (AI) enables machines to perceive, understand, process, and respond to inputs from the world around them just like human beings. Over the last few years, AI has become increasingly more sophisticated because of the efforts of a few leading companies. According to the 2020 McKinsey Global Survey on Artificial Intelligence (AI) , 50% of companies have reported using AI in at least one business function. 

Use cases for this technology range from automated customer service to predictive sales forecasting and healthcare innovations. Any task that involves high volume data processing or iterative human efforts can gain from AI. Let’s look at some of the top-most companies engaged in developing artificial intelligence in 2022 which apply the technology to myriad use cases. They are also among the most preferred employers for AI professionals. Here are the top 10 companies listed in alphabetical order. 

Headquarters and locations: With its headquarters in Seattle, Washington, U.S, the company has offices in a number of U.S. cities and around the world. It has 235 offices worldwide. 

Overview: Amazon develops AI for voice recognition and cloud-based machine learning on Amazon Web Services (AWS) . Founded in 1994, the company specializes in ecommerce and cloud technology. It ventured into AI in 2015-2016, when it launched its voice recognition-enabled range of Alexa devices. The company also conducts an AI innovation contest with prizes up to $500,000. Amazon’s last reported annual revenue for 2020 was $386 billion, which includes revenues from AI. 

Services offered: Amazon provides its customers with the following core AI services:

• Customers can use ML on the AWS cloud to build custom solutions. 
• It provides a scalable and flexible framework for ML developers through SageMaker. 
• Amazon Lex is the company’s conversational AI tool for chatbots.

Cost of services: SageMaker has a free tier for the first few months and follows capacity-based pricing thereafter. Lex costs $0.004 per speech request and $0.00075 per text request.

What makes it the best: Amazon’s AI solutions assure proven outcomes, as the company itself uses AI for its online shopping experience, Amazon Alexa offerings, and other products. It is also designed for scale, thanks to the company’s industry-leading cloud infrastructure. Those new to AI can kickstart deployment using Amazon’s pre-built ML solutions via Amazon SageMaker JumpStart.

Headquarters and : With its headquarters in Redwood City, California, U.S, C3.ai has five U.S. offices across Chicago, Houston, New York City, Redwood City, and Washington D.C. It also has international offices in eight other countries. 

Overview: C3.ai provides enterprise AI solutions to consolidate data, build AI models, generate predictions, and execute pre-built or customized applications. Entrepreneur and American businessman, Thomas Siebel, founded C3 in 2009. The company filed for an initial public offering (IPO) in 2020. C3.ai’s last reported annual revenue for 2021 was $183.2 million. 

Services offered: C3.ai offers the following services:

• It offers an end-to-end AI application development platform with a model-driven architecture. 
• It provides data scientists with a no-code AI development platform, C3 AI Ex Machina. 
• Customers can gain from a suite of pre-built AI apps for industries like manufacturing, financial services, and the public sector, or for functional verticals like supply chain management and customer management. 

Cost of services: Most of its offerings are custom priced, while the C3 AI Ex Machina platform starts at $25 per user per month.

What makes it the best: C3.ai caters to enterprise AI requirements at every level of digital maturity. Its entry-level, no-code platform is transparently priced on a software as a service (SaaS) model. You can also develop highly complex applications using the company’s data integration and model ops studio. 

3. DeepMind

Headquarters and locations: In addition to its London headquarters, DeepMind has offices in Canada, France, and the U.S. 

Overview: DeepMind is an AI research and development company that operates as a subsidiary of Alphabet. It also develops AI for positive outcomes in the healthcare sector. British academics Demis Hassabis, Shane Legg, and Mustafa Suleyman founded the company in 2010. The startup trained its AI algorithms on old games from the ‘70s and ‘80s to make it incrementally more intelligent over time. Google acquired DeepMind for $500 million in 2014. DeepMind’s last reported annual revenue for 2020 was $1.13 billion.

Services offered: DeepMind provides the following services:

• It develops advanced AI that can take on human challenges, such as winning in a game of Go or understanding complex data structures like stories and family trees. 
• It develops the WaveNet voice technology, which powers Google Assistant and the Google Cloud Platform . 
• Its AlphaFold tool can study 3D models of protein structures to accelerate medical research. 

Cost of services: The commercial cost of DeepMind’s services is undisclosed. 

What makes it the best: DeepMind is engaged in foundational research in the AGI segment and has made major strides in this domain. Its ML can be used with minimal supervision. The company also pays special attention to safe and ethical AI use. 

See More: Top 5 Businesses That AI Transformed

Headquarters and locations: Apart from the Mountain View headquarters, H2O.ai has U.S. offices in New York City as well as a presence in Canada, India, Singapore, and the Czech Republic. 

Overview: H2O.ai provides a cloud platform that democratizes AI through pre-built models, a ready-to-use app store, and low-code development. Founded in 2012, the company has raised $251.5 million in funding to date and is valued at $1.6 billion. Its two last rounds were led by large banks (the Commonwealth Bank of Australia and Goldman Sachs), underscoring the company’s operations in the financial services domain. The company’s estimated annual revenue is $47.4 million. H2O.ai is still in the funding stage, having recently raised $100 million in November 2021. 

Services offered: H2O.ai provides its customers with the following core services:

• The flagship H2O.ai cloud platform lets you develop AI models and apps through its automated machine learning (autoML) capabilities.
• Customers can improve their AI applications through the H2O.ai Feature Store. 
• It offers a ready-to-implement AI tool for document management that classifies documents, extracts content, and helps label the extracted data.

Cost of services: It has an open-source version that you can use for free, and commercial offerings are custom-priced. 

What makes it the best: H2O.ai streamlines several of the most common challenges in AI development. For instance, it has tools to check feature drift so that your models remain accurate. It can also automatically detect bias in application features. The company also offers a free-forever autoML tool that can be used in Hadoop, Spark, and Kubernetes environments. 

Headquarters and locations: The company is headquartered in Armonk, New York, U.S. It  has offices in all major regions, including North America, Latin America, Europe, the Middle East, Africa, and the Asia Pacific. In total, it has 130+ offices in 97 countries. 

Overview: IBM is primarily known for its proprietary AI engine, Watson, which is used in both research and commercial products. It offers AI for decision-making, language processing, and intelligent task automation . It initially designed Watson to compete with humans in games like Jeopardy. Today, Watson can be integrated into nearly every workflow, from HR to finance and supply chain management. The company’s last reported annual revenue for 2020 was $73.6 billion, of which the IBM Watson Health division generated approximately $1 billion. 

Services offered: IBM provides the following AI services:

• IBM has partnered with MIT to build the Watson AI lab for healthcare, security, and finance research. 
• The company has dedicated teams to cater to AI hardware, AI testing, conversational AI, computer vision, explainable AI, and 10+ other functions. 
• Customers can gain from pre-built Watson apps for supply chain management, healthcare, HR, business operations, and advertising. 

Cost of services: Pricing for IBM Watson Assistant starts at $140 per month, and pricing for Watson Studio starts at $99 per instance. 

What makes it the best: IBM Watson was among the first-ever AI systems to be developed that could answer questions in a natural language. The company offers an extensive catalog of ready-to-use products based on Watson, reducing your AI development and configuration needs. It also allows you to choose between on-premise and cloud deployment. 

6. Meta Platforms

Headquarters and locations: With headquarters in Menlo Park, California, U.S., Meta Platforms (formerly Facebook) has offices in 80+ cities across North America, Latin America, Europe, the Middle East, Africa, and the Asia Pacific regions.

Overview: Meta Platforms develops artificial intelligence for immersive technologies and social media. It has several AI initiatives in the works, from AI for naturalized interactions in virtual reality environments to AI detectors for harmful content. 

Founded in 2004, the company has a long history of working with AI tools, using the technology to match people, products, and content. In addition to its existing capabilities, Meta recently announced an AI supercomputer that can work across hundreds of languages, develop augmented reality tools, and pave the way towards the metaverse. The company’s last reported annual revenue for 2020 was $85.97 billion. 

Services offered: Meta Platforms provides the following AI services:

• The company develops AI-based face recognition for use across its social media platforms. 
• Meta Platforms’ AI enables more realistic interactions in augmented and virtual reality, part of its Oculus offerings. 
• It recently developed an AI algorithm for hate speech recognition based on a new architecture called Linformer, which manages the need for processing power as the complexity and length of input increases. 

Cost of services: Meta Platforms’ social media offerings are free for consumers. Businesses can use its AI for social media advertising on a custom-priced model, and Oculus for Business starts at $799. 

What makes it the best: The company has open-sourced several tools, frameworks, and research material it has developed over the years. AI developers can rely on its open-source library to power more versatile and robust AI by simply signing up for GitHub. 

Headquarters and locations: With headquarters in Ra’anana, Israel, NICE has offices in the Americas, Asia Pacific region, and across Europe, the Middle East, and Africa (EMEA). 

Overview: NICE develops AI technology for contact centers and customer experience management. It is primarily a customer relationship management (CRM) company focusing on data-driven operations. It applies AI to optimize contact centers, drive predictive decision-making, and help brands understand their customers. The company recently announced the Enlighten AI tool that automates everyday customer-facing tasks. The company’s last reported annual revenue for 2020 was $1.65 billion. 

Services offered: NICE provides the following AI services:

• It offers CXone SmartReach, a conversational AI system for proactive customer engagement. 
• It helps incorporate AI-driven self-service across the customer journey. 
• It also provides enterprises with AI and ML-driven customer experience analytics to identify opportunities and pain points. 

Cost of services: Pricing for its flagship product, NICE CXone, is undisclosed, but you can access a free trial for 60 days. 

What makes it the best: NICE offers a sophisticated and turnkey product for enterprises looking to adopt AI to improve customer experiences. It uses AI in various ways, from conversational chatbots to journey analytics, from AI-based call routing to workforce intelligence. Almost all its products incorporate AI seamlessly, providing you with better business outcomes. 

See More: 10 Most Common Myths About AI

Headquarters and locations: In addition to its San Francisco headquarters, OpenAI has an office in Chicago, Illinois. 

Overview: OpenAI LP (a for-profit company) develops artificial intelligence models that can be deployed via open-source APIs. This primarily comprises AI-based language and code generation. It is part of the parent organization, a non-profit called OpenAI INC. Its founders include Elon Musk from Tesla, Samuel H. Altman from YCombinator, Greg Brockman from Stripe, and several AI specialists. It launched a new learning model in 2022 called InstructGPT.

The company’s estimated annual revenue is $27.4 million. 

Services offered: OpenAI provides the following AI services:

• Its AI technology is used in popular language analysis applications, such as the new language app, Duolingo. 
• It can be used to translate the natural language to code for assisted programming use cases. 
• OpenAI can be leveraged for intelligent copywriting tools, text classification, translation, and other business problems related to a natural language. 

Cost of services: Pricing for the OpenAI API (commercial usage) starts at $0.0008 per 1000 tokens, where 1000 tokens are equal to approximately 750 words. It is available in four models — Ada, Babbage, Curie, and Davinci. 

What makes it the best: OpenAI’s free and open-source models have been instrumental in powering innovation at Microsoft, IBM, Salesforce, Cisco, and Intel, owing to its scalability and high accuracy. Its recently launched deep learning system called GPT-3 drives language auto-completion for both images and text. 

9. SenseTime

Headquarters and locations: Along with its Hong Kong headquarters, SenseTime has multiple offices in China. It has branches in Indonesia, Japan, South Korea, the United Arab Emirates, and many other countries. 

Overview: SenseTime builds AI technologies for business operations, smart cities, smart homes, and smart cars. The company was founded by Tang Xiao and Xu Li in 2014, who were both researchers at that time. They developed multiple white papers outlining the future possibilities of AI technology and eventually partnered with Qualcomm in 2017. The company is one of the founding members of the Global Artificial Intelligence Academic Alliance (GAIAA). Its last reported annual revenue for 2020 was 3.4 billion yuan or approximately $534 million.

Services offered: SenseTime provides the following AI services:

• Its solutions are built on the SenseCore universal AI infrastructure, which includes a hardware computing environment, a deep learning platform, and a model library. 
• It can be used for content-related tasks like content generation and enhancement. 
• It has a wide range of products and services for smart living, ranging from AI in healthcare , automated vehicles, and AI-based photo and video processing software. 

Cost of services: Pricing for SenseTime is undisclosed but you can apply for a trial for most offerings. 

What makes it the best: SenseTime is a research-driven company and is among the largest in Asia in terms of revenue. It partners closely with governments and companies to improve their efficiency through advanced computer visioning. It is one of the top companies developing AI for smart cities today, and its SenseFoundry Enterprise offering makes these capabilities available via AI as a Service. 

10. Salesforce

Headquarters and locations: With headquarters in San Francisco, California, U.S., Salesforce has 50+ offices worldwide, spanning the U.S., Asia Pacific, Latin America, and EMEA regions. 

Overview: Salesforce develops artificial intelligence for customer relationship management (CRM). This is delivered as an embedded layer of intelligence within the Salesforce platform, powered by its Einstein AI engine. Founded in 1999, the company has become almost synonymous with CRM and leverages AI for nearly every task. It launched Salesforce Einstein in 2016 as “artificial intelligence for everyone” and continues to build the technology while keeping in mind ethical considerations. In 2021, Salesforce debuted its AI Ethics model. The company’s last reported annual revenue for 2021 was $21.3 billion.

Services offered: Salesforce provides its customers with the following core services:

• Artificial intelligence simplifies everyday CRM activity on Salesforce, such as predicting the best time to send a customer email. 
• The company’s Einstein Platform Services allow developers to introduce AI capabilities like image recognition, sentiment, and intent analysis into Salesforce features with a single line of code. 
• Einstein can be leveraged for two main use cases — image recognition to solve object-related problems, and NLP for text-related use cases. 

Cost of services: Salesforce Service Cloud Einstein (an AI CRM system) starts at $50 per user per month, Einstein Predictions starts at $75 per user per month, and Einstein Platform Services are custom priced. 

What makes it the best: Salesforce provides the flexibility to leverage AI in any way you choose. You could build an Einstein-based platform tailored for your company. Or, you could implement Einstein AI tools for specific CRM tasks like sales forecasting, engagement scoring, product recommendations, and much more. 

See More: Top 5 AI Programming Languages for Beginners

Comparison of the Top AI Companies in 2022

Let’s quickly look at the comparison of the above companies.

Artificial intelligence and related areas like machine learning are among the most in-demand technologies today. Gartner’s recent report titled Emerging Technologies: AI Technology Spending in 2021 — Survey Trends found that vendors are increasing their investment in AI. One in three technology providers will invest $1 million or more in AI to cater to enterprise demand in the next two years. 

The industry, valued at $62.35 billion in 2020, is expected to grow at a staggering pace of 40.2% between 2021 and 2028, as per a study by Grand View Research. These ten companies are leading this wave with breakthrough AI innovations, practical applications for today’s most urgent business problems, and steadfast attention to AI ethics. 

Which is the fastest-growing AI company in 2022? Tell us on LinkedIn Opens a new window , Twitter Opens a new window , or Facebook Opens a new window . We’d love to hear from you! 

MORE ON ARTIFICIAL INTELLIGENCE

• What Is General Artificial Intelligence (AI)? Definition, Challenges, and Trends
• What Is Super Artificial Intelligence (AI)? Definition, Threats, and Trends
• Top 10 Open Source Artificial Intelligence Software in 2021
• How to Build a Career in AI and Machine Learning?
• What Are the Types of Artificial Intelligence: Narrow, General, and Super AI Explained

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Publications and Talks by Gleb Chuvpilo

• AI Research Rankings 2022: Sputnik Moment for China? , Gleb Chuvpilo May 2022 [ Link ]
• Keynote at AI Summit 2021 Seoul: State of AI 2021 , Gleb Chuvpilo December 2021 [ PDF ] [ AI Summit Seoul Home ]
• AI Research Rankings 2020: Can the United States Stay Ahead of China? , Gleb Chuvpilo December 2020 [ Link ] [ Dataset: Rankings CSV , Dataset CSV ]
• AI Research Rankings 2020: Can the United States Stay Ahead of China? [in Japanese: 2020年のAI研究ランキング：アメリカは中国をリードし続けられるのか？【前編】（各種ランキング）] , Gleb Chuvpilo December 2020 [ Link ]
• Who’s Ahead in AI Research at NeurIPS 2020? Insights and AI Research Rankings at the Leading AI Conference [in Japanese: 誰がNeurIPS 2020でAI研究を先導しているのか？リーディングAIカンファレンスの考察とAI研究ランキング] , Gleb Chuvpilo (translated by Koki Yoshimoto) , November 2020 [ Link ]
• Who’s Ahead in AI Research at NeurIPS 2020? Insights and AI Research Rankings at the Leading AI Conference , Gleb Chuvpilo November 2020 [ Link ] [ Dataset: NeurIPS 2020 rankings , NeurIPS 2020 dataset ]
• Who's Ahead in AI Research in 2020? Insights from the International Conference on Machine Learning (ICML 2020) , Gleb Chuvpilo July 2020 [ Link ] [ Dataset: ICML 2020 rankings , ICML 2020 dataset ]
• Who's Ahead in AI Research in 2020? Insights from the International Conference on Machine Learning (ICML 2020) [in Japanese: 2020年のAI研究は誰が先導しているか？国際機械学習会議（ICML 2020）の考察から] , Gleb Chuvpilo (translated by Koki Yoshimoto) July 2020 [ Link ]
• AI Research Rankings 2019: Insights from NeurIPS and ICML, Leading AI Conferences , Gleb Chuvpilo December 2019 [ Link ] [ Dataset: affiliations.csv , titles.csv ]
• AI Research Rankings 2019: Insights from NeurIPS and ICML, Leading AI Conferences [in Japanese: AI研究ランキング2019：世界を主導するAIカンファレンスであるNeurIPSとICMLの考察から] , Gleb Chuvpilo (translated by Koki Yoshimoto) December 2019 [ Part 1 ] [ Part 2 ]
• Who's Ahead in AI Research? Insights from NIPS, Most Prestigious AI Conference , Gleb Chuvpilo August 2018 [ Link ] [ Dataset: nips2017.csv ]
• Strategic Analysis of Jet.com Acquisition by Walmart , Gleb Chuvpilo, Raphaela DiClemente, Sunil Mulani, and Phin Reisz Wharton MGMT 721: Corporate Development, Mergers and Acquisitions. Final Project. The Wharton School at the University of Pennsylvania, April 2017. [ PDF ]
• Comparative Analysis of NYSE and NASDAQ Operations Strategy , Gleb Chuvpilo and Yanto Muliadi Wharton OIDD 615: Operations Strategy. Final Project. The Wharton School at the University of Pennsylvania, May 2016. [ PDF ]
• US Patent: Methods and Systems for Scheduling a Shared Ride Among Commuters , Oscar Salazar, Maria Verdugo, Gleb Chuvpilo, Gustavo Barron, Olivia Falcony, Ann Fandozzi Ride, October 2016. [ PDF ]
• Unconstrained Route Matching Algorithm (Technical Report 2) , Gleb Chuvpilo, Oscar Salazar, Sanny Liao, Sergio Botero, Nicolas Hock Ride, May 2015. [ PDF ]
• Route Matching Algorithm (Technical Report 1) , Gleb Chuvpilo, Sanny Liao, Oscar Salazar, Sergio Botero Ride, October 2014. [ PDF ]
• High-Bandwidth Packet Switching on the Raw General-Purpose Architecture , Gleb A. Chuvpilo and Saman Amarasinghe In Proceedings of the International Conference on Parallel Processing (ICPP-03), Kaohsiung, Taiwan, Republic of China, October 6-9, 2003. [ PDF ]
• A Simulation Based Comparison Between XCP and HighSpeed TCP , Gleb A. Chuvpilo and Jae Wook Lee, MIT 6.829 Computer Networks (Prof. Hari Balakrishnan). Final Project. Massachusetts Institute of Technology, Cambridge, Massachusetts, December, 2002. [ PDF ]
• High-Bandwidth Packet Switching on the Raw General-Purpose Architecture , Gleb A. Chuvpilo, S.M. Thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, August, 2002. [ PDF ]
• Tom and Jerry Robots , Gleb A. Chuvpilo and Jessica Howe, MIT 6.836 Embodied Intelligence (Prof. Rodney Brooks). Final Project. Massachusetts Institute of Technology, Cambridge, Massachusetts, May, 2002. [ PDF ]
• Ant Farm Genetic Algorithm , Gleb A. Chuvpilo, MIT 6.836 Embodied Intelligence (Prof. Rodney Brooks). Research Assignment 4. Massachusetts Institute of Technology, Cambridge, Massachusetts, April, 2002. [ PDF ] [ Assignment (PDF) ]
• RawNet: Network Processing on the Raw Processor , David Wentzlaff, Gleb A. Chuvpilo, Arvind Saraf, Saman Amarasinghe, and Anant Agarwal, In Research Abstracts of the MIT Laboratory for Computer Science, Cambridge, Massachusetts, March 2002. [ PDF ]
• Gigabit IP Routing on Raw , Gleb A. Chuvpilo, David Wentzlaff, and Saman Amarasinghe, In Proceedings of the 1st HPCA Workshop on Network Processors, Cambridge, Massachusetts, February 3, 2002. [ PDF ]

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• 19 June 2024
• Correction 19 June 2024

Not all ‘open source’ AI models are actually open: here’s a ranking

• Elizabeth Gibney

You can also search for this author in PubMed   Google Scholar

Technology giants such as Meta and Microsoft are describing their artificial intelligence (AI) models as ‘open source’ while failing to disclose important information about the underlying technology, say researchers who analysed a host of popular chatbot models.

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doi: https://doi.org/10.1038/d41586-024-02012-5

Updates & Corrections

Correction 19 June 2024 : An earlier version of this article contained an error in the table. This has now been corrected.

Liesenfeld, A. & Dingemanse, M. In FAccT '24: Proc. 2024 ACM Conf. on Fairness, Accountability, and Transparency 1774–1787 (ACM, 2024).

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THE AI INDEX REPORT

Measuring trends in Artificial Intelligence

ai iNDEX anNUAL rEPORT

Welcome to the 2023 AI Index Report

The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. The annual report tracks , collates , distills , and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind. The AI Index collaborates with many different organizations to track progress in artificial intelligence. These organizations include: the Center for Security and Emerging Technology at Georgetown University, LinkedIn, NetBase Quid, Lightcast, and McKinsey. The 2023 report also features more self-collected data and original analysis than ever before. This year’s report included new analysis on foundation models, including their geopolitics and training costs, the environmental impact of AI systems, K-12 AI education, and public opinion trends in AI. The AI Index also broadened its tracking of global AI legislation from 25 countries in 2022 to 127 in 2023.

TOP TAKEAWAYS

• Industry races ahead of academia.

Until 2014, most significant machine learning models were released by academia. Since then, industry has taken over. In 2022, there were 32 significant industry-produced machine learning models compared to just three produced by academia. Building state-of-the-art AI systems increasingly requires large amounts of data, compute, and money, resources that industry actors inherently possess in greater amounts compared to nonprofits and academia.

• Performance saturation on traditional benchmarks.

AI continued to post state-of-the-art results, but year-over-year improvement on many benchmarks continues to be marginal. Moreover, the speed at which benchmark saturation is being reached is increasing. However, new, more comprehensive benchmarking suites such as BIG-bench and HELM are being released.

• AI is both helping and harming the environment.

New research suggests that AI systems can have serious environmental impacts. According to Luccioni et al., 2022, BLOOM’s training run emitted 25 times more carbon than a single air traveler on a one-way trip from New York to San Francisco. Still, new reinforcement learning models like BCOOLER show that AI systems can be used to optimize energy usage.

• The world’s best new scientist … AI?

AI models are starting to rapidly accelerate scientific progress and in 2022 were used to aid hydrogen fusion, improve the efficiency of matrix manipulation, and generate new antibodies.

• The number of incidents concerning the misuse of AI is rapidly rising.

According to the AIAAIC database, which tracks incidents related to the ethical misuse of AI, the number of AI incidents and controversies has increased 26 times since 2012. Some notable incidents in 2022 included a deepfake video of Ukrainian President Volodymyr Zelenskyy surrendering and U.S. prisons using call-monitoring technology on their inmates. This growth is evidence of both greater use of AI technologies and awareness of misuse possibilities.

• The demand for AI-related professional skills is increasing across virtually every American industrial sector.

Across every sector in the United States for which there is data (with the exception of agriculture, forestry, fishery and hunting), the number of AI-related job postings has increased on average from 1.7% in 2021 to 1.9% in 2022. Employers in the United States are increasingly looking for workers with AI-related skills.

• For the first time in the last decade, year-over-year private investment in AI decreased.

Global AI private investment was $91.9 billion in 2022, which represented a 26.7% decrease since 2021. The total number of AI-related funding events as well as the number of newly funded AI companies likewise decreased. Still, during the last decade as a whole, AI investment has significantly increased. In 2022 the amount of private investment in AI was 18 times greater than it was in 2013.

• While the proportion of companies adopting AI has plateaued, the companies that have adopted AI continue to pull ahead.

The proportion of companies adopting AI in 2022 has more than doubled since 2017, though it has plateaued in recent years between 50% and 60%, according to the results of McKinsey’s annual research survey. Organizations that have adopted AI report realizing meaningful cost decreases and revenue increases.

Policymaker interest in AI is on the rise.

An AI Index analysis of the legislative records of 127 countries shows that the number of bills containing “artificial intelligence” that were passed into law grew from just 1 in 2016 to 37 in 2022. An analysis of the parliamentary records on AI in 81 countries likewise shows that mentions of AI in global legislative proceedings have increased nearly 6.5 times since 2016.

Chinese citizens are among those who feel the most positively about AI products and services. Americans … not so much.

In a 2022 IPSOS survey, 78% of Chinese respondents (the highest proportion of surveyed countries) agreed with the statement that products and services using AI have more benefits than drawbacks. After Chinese respondents, those from Saudi Arabia (76%) and India (71%) felt the most positive about AI products. Only 35% of sampled Americans (among the lowest of surveyed countries) agreed that products and services using AI had more benefits than drawbacks.

Chapter 1: Research and Development

This chapter captures trends in AI R&D. It begins by examining AI publications, including journal articles, conference papers, and repositories. Next it considers data on significant machine learning systems, including large language and multimodal models. Finally, the chapter concludes by looking at AI conference attendance and open-source AI research. Although the United States and China continue to dominate AI R&D, research efforts are becoming increasingly geographically dispersed.

• The United States and China had the greatest number of cross-country collaborations in AI publications from 2010 to 2021, although the pace of collaboration has since slowed.
• AI research is on the rise, across the board.
• China continues to lead in total AI journal, conference, and repository publications.
• Large language models are getting bigger and more expensive.

Chapter 2: Technical Performance

This year’s technical performance chapter features analysis of the technical progress in AI during 2022. Building on previous reports, this chapter chronicles advancement in computer vision, language, speech, reinforcement learning, and hardware. Moreover, this year this chapter features an analysis on the environmental impact of AI, a discussion of the ways in which AI has furthered scientific progress, and a timeline-style overview of some of the most significant recent AI developments.

• Generative AI breaks into the public consciousness.
• AI systems become more flexible.
• Capable language models still struggle with reasoning.
• AI starts to build better AI.

Chapter 3: Technical AI Ethics

Fairness, bias, and ethics in machine learning continue to be topics of interest among both researchers and practitioners. As the technical barrier to entry for creating and deploying generative AI systems has lowered dramatically, the ethical issues around AI have become more apparent to the general public. Startups and large companies find themselves in a race to deploy and release generative models, and the technology is no longer controlled by a small group of actors. In addition to building on analysis in last year’s report, this year the AI Index highlights tensions between raw model performance and ethical issues, as well as new metrics quantifying bias in multimodal models.

• The effects of model scale on bias and toxicity are confounded by training data and mitigation methods.
• Generative models have arrived and so have their ethical problems.
• Fairer models may not be less biased.
• Interest in AI ethics continues to skyrocket.
• Automated fact-checking with natural language processing isn’t so straightforward after all.

Chapter 4: The Economy

Increases in the technical capabilities of AI systems have led to greater rates of AI deployment in businesses, governments, and other organizations. The heightening integration of AI and the economy comes with both excitement and concern. Will AI increase productivity or be a dud? Will it boost wages or lead to the widespread replacement of workers? To what degree are businesses embracing new AI technologies and willing to hire AI-skilled workers? How has investment in AI changed over time, and what particular industries, regions, and fields of AI have attracted the greatest amount of investor interest? This chapter examines AI-related economic trends by using data from Lightcast, LinkedIn, McKinsey, Deloitte, and NetBase Quid, as well as the International Federation of Robotics (IFR). This chapter begins by looking at data on AI-related occupations and then moves on to analyses of AI investment, corporate adoption of AI, and robot installations.

• Once again, the United States leads in investment in AI.
• In 2022, the AI focus area with the most investment was medical and healthcare ($6.1 billion); followed by data management, processing, and cloud ($5.9 billion); and Fintech ($5.5 billion).
• AI is being deployed by businesses in multifaceted ways.
• AI tools like Copilot are tangibly helping workers.
• China dominates industrial robot installations.

Chapter 5: Education

Studying the state of AI education is important for gauging some of the ways in which the AI workforce might evolve over time. AI-related education has typically occurred at the postsecondary level; however, as AI technologies have become increasingly ubiquitous, this education is being embraced at the K–12 level. This chapter examines trends in AI education at the postsecondary and K–12 levels, in both the United States and the rest of the world. We analyze data from the Computing Research Association’s annual Taulbee Survey on the state of computer science and AI postsecondary education in North America, Code.org’s repository of data on K–12 computer science in the United States, and a recent UNESCO report on the international development of K–12 education curricula.

• More and more AI specialization.
• New AI PhDs increasingly head to industry.
• New North American CS, CE, and information faculty hires stayed flat.
• The gap in external research funding for private versus public American CS departments continues to widen.
• Interest in K–12 AI and computer science education grows in both the United States and the rest of the world.

Chapter 6: Policy and Governance

The growing popularity of AI has prompted intergovernmental, national, and regional organizations to craft strategies around AI governance. These actors are motivated by the realization that the societal and ethical concerns surrounding AI must be addressed to maximize its benefits. The governance of AI technologies has become essential for governments across the world. This chapter examines AI governance on a global scale. It begins by highlighting the countries leading the way in setting AI policies. Next, it considers how AI has been discussed in legislative records internationally and in the United States. The chapter concludes with an examination of trends in various national AI strategies, followed by a close review of U.S. public sector investment in AI.

• From talk to enactment—the U.S. passed more AI bills than ever before.
• When it comes to AI, policymakers have a lot of thoughts.
• The U.S. government continues to increase spending on AI.
• The legal world is waking up to AI.

Chapter 7: Diversity

AI systems are increasingly deployed in the real world. However, there often exists a disparity between the individuals who develop AI and those who use AI. North American AI researchers and practitioners in both industry and academia are predominantly white and male. This lack of diversity can lead to harms, among them the reinforcement of existing societal inequalities and bias. This chapter highlights data on diversity trends in AI, sourced primarily from academia. It borrows information from organizations such as Women in Machine Learning (WiML), whose mission is to improve the state of diversity in AI, as well as the Computing Research Association (CRA), which tracks the state of diversity in North American academic computer science. Finally, the chapter also makes use of Code.org data on diversity trends in secondary computer science education in the United States. Note that the data in this subsection is neither comprehensive nor conclusive. Publicly available demographic data on trends in AI diversity is sparse. As a result, this chapter does not cover other areas of diversity, such as sexual orientation. The AI Index hopes that as AI becomes more ubiquitous, the amount of data on diversity in the field will increase such that the topic can be covered more thoroughly in future reports.

• North American bachelor’s, master’s, and PhD-level computer science students are becoming more ethnically diverse.
• New AI PhDs are still overwhelmingly male.
• Women make up an increasingly greater share of CS, CE, and information faculty hires.
• American K–12 computer science education has become more diverse, in terms of both gender and ethnicity.

Chapter 8: Public Opinion

AI has the potential to have a transformative impact on society. As such it has become increasingly important to monitor public attitudes toward AI. Better understanding trends in public opinion is essential in informing decisions pertaining to AI’s development, regulation, and use. This chapter examines public opinion through global, national, demographic, and ethnic lenses. Moreover, we explore the opinions of AI researchers, and conclude with a look at the social media discussion that surrounded AI in 2022. We draw on data from two global surveys, one organized by IPSOS, and another by Lloyd’s Register Foundation and Gallup, along with a U.S-specific survey conducted by PEW Research. It is worth noting that there is a paucity of longitudinal survey data related to AI asking the same questions of the same groups of people over extended periods of time. As AI becomes more and more ubiquitous, broader efforts at understanding AI public opinion will become increasingly important.

• Chinese citizens are among those who feel the most positively about AI products and services. Americans … not so much.
• Men tend to feel more positively about AI products and services than women. Men are also more likely than women to believe that AI will mostly help rather than harm.
• People across the world and especially America remain unconvinced by self-driving cars.
• Different causes for excitement and concern.
• NLP researchers … have some strong opinions as well.

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Best AI Chatbots of 2024

ChatGPT isn't the only free AI chatbot on the net. We tested and compared them to figure out the best one for you.

The launch of AI chatbot ChatGPT in late 2022 completely transformed how we interact with technology. Generative AI can answer questions in WhatsApp chats , summarize emails in Outlook , create "genmojis" in iMessage and spit out answers to complex questions with ease. 

Now pretty much every major tech company has launched its own chatbot to compete with ChatGPT, including Google Gemini , Microsoft Copilot and Meta AI . Smaller startups also have working AI chatbots that compete well against trillion-dollar companies, including Anthropic's Claude and Perplexity.

At CNET, we reviewed all of those AI chatbots to find the best one for you (side note: doing so rewired my brain ). The list below focuses on free versions as opposed to paid ones, but note that most AI chatbots do have a paid tier that often performs better than the free version. For most people, however, the free chatbot will get you 90% of what you need. 

What is the best AI chatbot of 2024 so far?

Claude by Anthropic is the best AI chatbot overall right now. That doesn't mean ChatGPT or Perplexity are bad. Actually, both have their own advantages and disadvantages. Overall, though, the breadth at which Claude is able to answer questions and its calibration towards nuance and engagement should make it the most valuable to most people.

Best overall AI chatbot

Anthropic Claude

• Gives nuanced answers with detail
• Fast and well organized

Don't like

• Not connected to the internet
• Doesn't automatically provide sources

Claude by Anthropic is CNET Editors' Choice for the best overall AI chatbot . That doesn't mean it excels at every task compared to the competition. Rather, it does a consistent job and goes further than what's coming out of Google, Microsoft, Perplexity and OpenAI at the free tier. 

The major things holding Claude back are its apathetic linking to outside sources and the lack of an Android app. If Anthropic could better tune Claude to have access to the open internet to link to sources and shopping links, it'd make the chatbot a true one-stop-shop. Despite the omission, the quality of its responses and its willingness to engage in heady conversations make it the most useful overall. I also like how Claude is more willing to engage and ask the user questions.

Read our Claude review .

Second-best AI chatbot

ChatGPT Free

• Gives meaningful answers with a good amount of context
• Does most things well, including research, email writing and recommendations
• Could tap more into historical context for explanations
• Can be slow at times
• Asking for sources can be tedious

A close contender for the top spot is OpenAI's ChatGPT-4o, which is now available for free , albeit with caveats. OpenAI says that while free users will have access to its ChatGPT-4o model, when usage limits are reached based on demand, then free users will revert back to the older 3.5 model. While free users are able to ask ChatGPT-4o up to 40 messages every three hours , that number might be reduced due to high demand. 

ChatGPT Free offers detailed and nuanced answers, but they weren't quite as high-quality as Claude. Putting the two side-by-side, I noticed slight differences in the quality of answers. I particularly liked the specificity that Claude delved into when asking heavier political questions, such as the morality of the Israel-Palestine conflict. And like Claude, ChatGPT doesn't link to outside sources. Sometimes when you ask it to provide sources, it'll suggest things to Google or YouTube.

CNET doesn't have a full review of ChatGPT Free yet (that's coming soon), but I've tested it extensively enough to give it the second-place spot on this list.

Read our ChatGPT 4 review .

Best AI chatbot for research

• Includes list of all sources used
• Gives nuanced answers in an easy-to-follow list
• Too much reliance on Reddit posts and forums, which aren't citable for most people

Perplexity did the best job for research in my testing. The team at Perplexity has tuned its AI chatbot to add loads of links into answers. Hyperlinks can include journalistic publications, Reddit posts and even YouTube videos. 

When writing essays or articles, links to actual sources are critical. Perplexity actually lists each source in a handy sidebar that can be easily accessed. And, thankfully, the sources aren't simply Wikipedia, which won't fly with your college professor. The only downside is that Perplexity does rely on forum posts and Reddit for its answers, which aren't journalistic or scholarly. I'm sure the information is handy, but that will mean doing more research on your part to ensure those factoids are accurate and can be sourced to something more attributable.

Read our Perplexity review .

Best AI chatbot for shopping

Google Gemini

• Gives solid shopping recommendations and product research
• Links to Amazon products directly
• Connected to open internet with option to double-check against Google Search
• Can make stuff up
• Doesn't answer questions on difficult subject matters

Google's AI engine has been prone to hallucinations -- simply making up stuff -- such as when Google's AI overviews feature was rolled back last month when it suggested people eat rocks . When I reviewed Gemini earlier this year, it was the lowest-rated AI chatbot out of the bunch, with a dismal 5/10 score. 

But AI chatbots aren't stationary pieces of technology that exist in a vacuum. Gemini has improved since I reviewed it back in April, although it still hallucinates. In my recent testing, for example, Gemini made up the name of a college professor and the name of an Adult Swim executive. And it simply refuses to answer heavier political questions, as does Microsoft's Copilot. 

But the one area in which Gemini did excel was in how-to guides and shopping recommendations. When I asked how to cut a perfect circle in a piece of vinyl, not only did Gemini give a list of instructions, it also linked to products on Amazon that could make the process easier. None of the other chatbots linked to Amazon products. When it came to shopping recommendations, Gemini gave quick and concise answers with links to where to buy products.

Read our Google Gemini review .

How we tested AI chatbots

Testing for AI requires constant tweaking. Because companies are always looking at ways to improve their AI models, tests that worked to push AI chatbots last year or even last month might not work today. That said, we try to test AI chatbots with questions we believe normal people will ask. We aren't necessarily trying to "break" AI chatbots with obtuse-sounding questions meant to confuse. Instead, we consider what might be asked when it comes to video game guides or shopping recommendations. Our tests also ask some heavier questions about difficult events happening around the world to see which are comfortable in actually engaging. 

The AI chatbots that sit on this list, generally, are able to take on the tougher questions and give believable answers with nuance. Like reading an article written by a university professor, we want AI chatbots to have that same level of consideration for historical context and competing interests to try and leave the reader with a better understanding from a higher-level perspective. 

For more, check out How We Test AI .

Factors to consider

When using an AI chatbot, keep your privacy and sensitive information in mind. For example, it might seem benign to have an AI chatbot summarize your company's meeting notes . But, that data could inadvertently be used to train AI models further, and you've essentially lost control of it, according to experts. Plus, it's totally within the realm of the privacy policies for AI companies to sell that data to third parties. While Google's privacy policy might state that it'll remove any personally identifiable information, it's still best to err on the side of caution. Google actually outright recommends you don't upload any confidential information whatsoever . 

Other AI chatbots we tested

Microsoft Copilot: This chatbot, found on the Bing search engine, uses GPT-4 Turbo, a version of OpenAI's GPT-4 that is optimized for speed. While Copilot is still a serviceable chatbot, it doesn't answer questions with the same level of detail and nuance as Claude, ChatGPT-4o and Perplexity. Plus, its outright refusal to answer questions that are politically sensitive in nature is a demerit.

Meta AI: Unlike other AI chatbots, Meta AI not only has its own dedicated webpage , but is integrated into Instagram, WhatsApp, Facebook and the Ray-Ban Meta smart glasses . When CNET's Katelyn Chedraoui reviewed Meta AI earlier this year, she found it to be decent overall, but noncompetitive with the competition. While Meta AI did provide good shopping advice with some cajoling, and excelled in recipes, it fell short in other areas. When it came to research, despite it being connected to Google and Bing, it sourced nonscholarly papers, like an elementary school lesson plan. 

ChatGPT 3.5 : This service, which I tested earlier in 2024, has since been replaced by what OpenAI calls ChatGPT Free (which utilizes a combination of GPT-4o, GPT-4 and GPT-3.5). It is a competent AI chat engine that answers difficult questions with easy-to-understand language. It doesn't hallucinate at the rate of Google Gemini, but there really isn't a reason to switch ChatGPT to 3.5 when you can use 4o and 4 for free. 

AI chatbot FAQs

Do i need to use ai.

AI is a handy tool and can be a timesaver, but it isn't necessary in day-to-day life. It's totally possible to still Google Search your queries and read through articles to get the answer you're looking for. Heck, it probably gives your brain more of a mental workout!

What is the best free AI?

Anthropic Claude is currently CNET's choice for the best free AI chatbot. Free versions of ChatGPT and Perplexity also offer great results with specific advantages and disadvantages. Google's Gemini is great for shopping recommendations. Like Gemini, Microsoft's CoPilot won't answer heavier and more controversial questions.

What is the best AI on mobile?

While there are mobile apps for Gemini, Copilot and Perplexity, we prefer the ChatGPT app the most. It has a clean interface and is easy to navigate. But really, any app will get the job done. Unfortunately, Claude only has a mobile app for iOS and not Android. 

Can AI be trusted?

Geoffrey Hinton, the researcher who developed the concept of neural networks and who is considered the godfather of AI, feels less enthusiastic about the technology he helped birth . As for using AI chatbots on a day-to-day basis, they're handy tools that can synthesize the world's information for you in seconds, saving you lots of research time. Just be aware that sometimes AI chatbots get things wrong and it's good to do a Google search for things that sound a bit dubious. Also, be careful when giving AI chatbots sensitive information. Don't ask an AI chatbot to summarize your company's trade secrets, as privacy policies give AI companies wide latitude to do with that data as they please.

The 10 Most Well-Funded AI Startups Of 2024 (So Far)

Cyera, Glean and xAI are among the recently founded AI companies capturing major investor attention.

A data security platform powered by artificial intelligence, a provider of AI agents and assistants founded on company data, and Elon Musk’s mysterious AI company are among the most well-funded startups so far in 2024.

Cyera, Glean and xAI are among the recently founded companies capturing investor attention as the AI market continues its rapid expansion, demonstrating that in the AI space innovation doesn’t happen solely within the tech giants.

The funding comes during a multi-year dry spell for venture capital, according to market intelligence platform AlphaSense. VC investment fell to $76 billion in the first quarter of 2024, the lowest since the second quarter of 2019. The number of deals hit a four-year low.

[RELATED: The 10 Hottest Cybersecurity Tools And Products Of 2024 (So Far) ]

AI Funding 2024

CRN compiled the following list by referencing data from startup funding websites Crunchbase and Pitchbook.

Some well-known and well-funded organizations did not make the list because they are not based in the United States or because the company was founded more than five years ago, beyond CRN’s definition of a startup. c

A series of startup funds associated with OpenAI – arguably the biggest AI organization in the space thanks to its ChatGPT content-generator – have raised money this year, according to filings with the United States Securities and Exchange Commission.

OpenAI Startup Fund SPV I raised $10 million, according to a February filing. An April filing filing showed that OpenAI Startup Fund SPV II raised $15 million, and a filing in May showed that OpenAI Startup Fund SPV III raised $5 million.

Multiple news outlets reported that Cohere, for example, raised $450 million in June with investors including Nvidia, Cisco and Salesforce, but the startup is based in Canada. CoreWeave raised $1.1 billion in a new round in May, in another example, but the company was founded in 2017.

Read on for the top AI startups of 2024 so far by venture capital raised.

10. Perplexity AI

HQ: San Francisco

CEO: Aravind Srinivas

2024 Amount Raised So Far: $136.3M

Perplexity started 2024 with the announced closure of a $73.6 million Series B round of funding, and then in April reveal that it raised another $62.7 million. Investors include billionaire Stanley Druckenmiller, Y Combinator CEO Garry Tan, Amazon founder Jeff Bezos and Nvidia.

Amazon CEO Andy Jassy notably shouted out Perplexity during the vendor’s 2024 first quarter earnings report.

The additional money will help with global expansion, according to a Perplexity statement. The startup positions its AI technology as an answer engine giving users conversational, verifiable information with citations from trusted news outlets, academic papers and established blogs.

The startup also used the announcement of its $62.7 million round to promote its new Perplexity Enterprise Pro business-to-business (B2B) offering. Perplexity serves 169 million queries per month. Multiple media outlets have also reported that Perplexity is seeking another $250 million.

CEO Aravind Srinivas co-founded the startup in 2022. He previously worked as a research scientist at OpenAI, according to his LinkedIn account. He has a doctorate in computer science.

In May, Perplexity introduced Pages, a tool for turning research into articles, reports or guides. The following month, news outlets Forbes and Wired accused the Perplexity feature of plagiarizing articles from their websites.

9. Celestial AI

HQ: Santa Clara, Calif.

CEO: Dave Lazovsky

2024 Amount Raised So Far: $175M

Celestial AI’s Photonic Fabric optical interconnect technology platform attracted investor interest earlier this year with the March announcement of a $175 million Series C round of funding.

U.S. Innovative Technology Fund (USIT), AMD Ventures, Koch Disruptive Technologies (KDT), Temasek, Samsung Catalyst, Porsche Automobil Holding and others participated in the round, which should help Celestial scale up its commercialization, the startup said in a statement.

Celestial bills its platform as disaggregating compute and memory, enabling greater bandwidth and memory capacity, and reducing latency and power consumption compared to optical interconnect alternatives and copper.

In a separate March statement, Celestial said that system integrators are part of the Photonic Fabric ecosystem it is building – including custom silicon design service providers such as Broadcom and packaging suppliers such as Samsung.

CEO Dave Lazovsky founded the startup in 2020, according to his LinkedIn account. He previously founded semiconductor and energy company Intermolecular, took it public and led Intermolecular as CEO until 2014.

8. Cognition AI

CEO: Scott Wu

2024 Amount Raised So Far: $196M

In June, Cognition AI CEO Scott Wu confirmed on Bloomberg TV that his startup raised $175 million in a round of funding – building on a $21 million round revealed by multiple media companies in March.

The San Francisco-based startup is behind Devin, an autonomous AI software engineer that can build and deploy applications end to end, find and fix bugs in codebases, and train and fine-tune models, among other actions, according to Cognition.

Also in June, Cognition upgraded Devin with playbooks for repetitive, multi-step engineering tasks. Devin also gained a machine snapshots feature for save states, event-driven triggering and the ability for users to read and edit Devin’s files.

Wu cofounded Cognition in 2023, according to his LinkedIn account. He previously cofounded AI-powered social media platform Lunchclub and served as its chief technology officer until 2022.

HQ: Palo Alto, Calif.

CEO: Arvind Jain

2024 Amount Raised So Far: $200M

Glean bills itself as the enterprise AI platform for company data, providing trusted answers grounded in users’ data with a centralized platform providing no-code, custom generative AI agents, assistants and chatbots with security, permissions and more.

The startup’s pitch won over investors to the tune of more than $200 million raised in a funding round, as revealed in a February blog post. Kleiner Perkins, Lightspeed Venture Partners, Sequoia Capital, Coatue, Iconiq Growth, Capital One Ventures, Citi, Databricks Ventures and Workday Ventures were among the round participants.

CEO Arvind Jain co-founded the startup in 2019, according to his LinkedIn account. He previously co-founded security vendor Rubrik and served as a distinguished engineer at Google, which he left in 2014.

In June, the startup made Glean Apps, Actions and APIs generally available for users to build custom agents and applications with advanced large language models (LLMs).

CEO: Scott Dietzen

2024 Amount Raised So Far: $227M

Augment closed a $227 million Series B round of funding in April, giving the startup more firepower for its brand of AI coding assistance technology.

The startup plans to use the capital “to accelerate product development and build out its product, engineering and go-to-market functions as the company gears up for rapid growth,” according to an Augment statement.

Sutter Hill Ventures, Index Ventures, Innovation Endeavors, Lightspeed Venture Partners and Meritech Capital participated in the round.

The startup positions its technology as optimized for large codebases, with suggestions that reflect a user’s coding patterns. Augment’s custom AI models avoid hallucinations, has security to protect intellectual property (IP) and is faster at inferences than competitors, according to the startup.

Augment – based in Palo Alto, Calif., and founded in 2022 – is led by CEO Scott Dietzen, who previously led Pure Storage as CEO for about seven years before leaving the job in 2017, according to his LinkedIn account.

HQ: New York

CEO: Yotam Segev

2024 Amount Raised So Far: $300M

Cyera closed a $300 million Series C round of funding in April, with investors including Coatue, Spark Capital, Georgian, AT&T Ventures, Sequoia, Accel and Redpoint.

The money will help the New York-based startup “continue to work tirelessly to enable security teams to discover and control their data - no matter where it resides - and in doing so transform data from a source of enterprise risk - to the lifeblood of business in the Digital Age.”

Cyera promises users an AI-powered platform for data security. The platform discovers, analyzes and classifies data across users’ data landscape without agents or overhead, according to Cyera’s website.

CEO Yotam Segev co-founded the startup in 2021, according to his LinkedIn account.

He previously served in cybersecurity leadership roles for more than 10 years as part of Unit 8200, the Israeli Defense Force’s signals intelligence service, according to an online bio.

In February, Cyera made its platform available on Google Cloud Marketplace, according to the vendor.

The company is part of CRN’s 2024 Partner Program Guide.

4. Figure AI

HQ: Sunnyvale, Calif.

CEO: Brett Adcock

2024 Amount Raised So Far: $675M

In February, Figure AI closed a $675 million Series B round of funding from investors including Microsoft, OpenAI Startup Fund, Nvidia, Jeff Bezos’ Bezos Expeditions and Intel Capital.

Sunnyvale, Calif.-based Figure and OpenAI also inked an agreement for developing AI models for humanoid robots, according to a statement at the time. Figure will also use Microsoft Azure for AI infrastructure, training and storage.

“The collaboration aims to help accelerate Figure's commercial timeline by enhancing the capabilities of humanoid robots to process and reason from language,” according to the statement. “This new capital will be used strategically for scaling up AI training, robot manufacturing, expanding engineering headcount, and advancing commercial deployment efforts.”

CEO Brett Adcock founded the startup in 2022. He previously founded aerospace company Archer Aviation and took it public in 2021.

Figure is at work on what it calls “the world’s first commercially viable autonomous humanoid robot,” expected to stand at 5-foot-6, carry a 44-pound payload, weigh 132 pounds, run for five hours and more at about 3 miles an hour, according to the startup’s website.

3. Xaira Therapeutics

CEO: Marc Tessier-Lavigne

2024 Amount Raised So Far: $1B

Xaira Therapeutics launched in April with more than $1 billion in committed capital to apply AI to pharmaceutical discovery and development.

The San Francisco-based startup wants to build “a platform for drug discovery and development that will advance multiple drug programs and unlock biological understanding to inform future discovery” through “advanced machine learning research, expansive data generation to power new models, and robust therapeutic product development,” according to a statement about the funding round.

Investors included ARCH Venture Partners and Foresite Capital, joined by F-Prime, NEA, Sequoia Capital, Lux Capital, Lightspeed Venture Partners, Menlo Ventures, Two Sigma Ventures, the Parker Institute for Cancer Immunotherapy (PICI), Byers Capital, Rsquared, and SV Angel, according to a statement by Xaira, which was founded in 2023.

The startup is led by Marc Tessier-Lavigne, who previously led Stanford University as its 11th president starting in 2016, according to an online bio. He also led Rockefeller University starting in 2011 as president, plus he served as head of the school’s Laboratory of Brain Development and Repair. He has a doctorate in physiology.

His time at Stanford ended in 2023 amid controversy over his past research and papers.

2. Anthropic

CEO: Dario Amodei

2024 Amount Raised So Far: $2.75B

The best-funded startup of 2024 so far is Anthropic, founded in 2021 by siblings Daniela and Dario Amodei, who previously worked at Microsoft-backed rival OpenAI.

In March, Amazon made good on its promised $4 billion investment in the AI startup, giving Anthropic the final $2.75 billion just before the expiration of the agreement.

Seattle-based cloud giant Amazon said in a blog post this month that it gave Anthropic the final portion of the $4-billion investment, first revealed in September as part of a deal for Anthropic to build, train and deploy AI models on Amazon Web Services’ Trainium and Inferentia chips while giving AWS customers access to Anthropic foundation models on the fully managed Amazon Bedrock service.

“The work Amazon and Anthropic are doing together to bring the most advanced generative artificial intelligence technologies to customers worldwide is only beginning,” according to the blog post.

The investment came after Anthropic unveiled its Claude 3 model family, promising increased capabilities in analysis, forecasting, nuanced content creation, code generation, conversing in non-English languages and other actions.

In a May article, TechCrunch reported that Anthropic has to date raised nearly $8 billion at an $18.4 billion valuation, with more than $7 billion raised in the last year. Its list of about 60 investors also includes Google, Salesforce, SAP and Zoom.

In June, Anthropic launched Claude 3.5 Sonnet, the first release in the forthcoming Claude 3.5 model family.

Anthropic has also shown an interest in partnering with solution providers, inking deals with Accenture and Boston Consulting Group (BCG).

HQ: Burlingame, Calif.

Leader: Elon Musk

2024 Amount Raised So Far: $6B

In May, Elon Musk’s AI startup xAI revealed that it closed a $6 billion series B round of funding with investors including Andreessen Horowitz, Sequoia Capital, Fidelity Management & Research Co., and Saudi Arabian billionaire Prince Alwaleed Bin Talal and his Kingdom Holding company.

An xAI statement on the funding round said that it “will continue on this steep trajectory of progress over the coming months, with multiple exciting technology updates and products soon to be announced.”

“The funds from the round will be used to take xAI’s first products to market, build advanced infrastructure, and accelerate the research and development of future technologies,” according to the statement.

Musk – CEO of Tesla and SpaceX and owner of X, formerly Twitter – founded xAI last year.

In May, xAI released a preview of a version of its Grok conversational AI model to X Premium users. This version adds visual information processing for diagrams, charts, screenshots, photographs and other content.

Hours worked

Hours worked is the total number of hours actually worked per year divided by the average number of people in employment per year.

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Actual hours worked include regular work hours of full-time, part-time and part-year workers, paid and unpaid overtime, hours worked in additional jobs. Hours excluded include time not worked because of public holidays, annual paid leave, own illness, injury and temporary disability, maternity leave, parental leave, schooling or training, slack work for technical or economic reasons, strike or labour dispute, bad weather, compensation leave and other reasons. The data cover employees and self-employed workers.

The data are intended for comparisons of trends over time; they are unsuitable for comparisons of the level of average annual hours of work for a given year, because of differences in sources and methods of calculation.

This indicator is measured in hours per worker per year.

• Labour markets surveillance
• Directorate for Employment, Labour and Social Affairs
• Measuring the labour market

Access the source dataset in Data Explorer

Related data.

• Indicator Labour productivity forecast Labour productivity is defined as real gross domestic product (GDP) per hour worked.
• Indicator Native-born participation rates The native-born participation rate is calculated as the share of employed and unemployed native-born persons aged 15-64 in the total native-born population (active and inactive persons) of that same age.
• Indicator Native-born employment The native-born employment rate is calculated as the share of employed native-born persons aged 15-64 in the total native-born population (active and inactive persons) of that same age.
• Indicator Employment rate Employment rate is the extent to which available labour resources (people available to work) are being used.
• OECD unemployment rate stable at 4.9% in April 2024 13 June 2024
• OECD unemployment rate stable at 4.9% but rising among women in March 2024 16 May 2024

60 SEO Statistics For 2024

Updated: Nov 28, 2023, 2:48pm

Table of Contents

Key seo statistics in 2024, search engine statistics, keyword statistics, ranking statistics, link building statistics, video search statistics, mobile seo statistics, voice search statistics, local seo statistics, seo industry statistics.

As a small business owner, SEO should be top of mind. This is particularly true if you’re on a budget and don’t want to spend thousands of dollars on paid ads. SEO can improve your website’s visibility and in turn, increase conversions and sales.

With a solid SEO strategy, you may also build brand awareness and trust with current and prospective customers. Whether you’re new to SEO, working with a third-party SEO service , or consider yourself a seasoned vet, these SEO statistics will provide some insight into the current state of the industry and how it might change in the future.

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Organic clicks accounted for 45.1% of all search result clicks in 2022

Out of the total search engine clicks on desktop devices, 45.1% came from organic clicks 6 . This shows that users are more likely to trust organic search results than those that come from other sources, such as paid ads. By taking advantage of SEO, you can win more traffic.

26% of searches resulted in no clicks

Not every search leads a user to a third-party website from an organic search result. In fact, 26% of searches resulted in zero clicks 6 . These zero-click searches were likely informational queries rather than commercial queries with high purchase intent. Users may find the information they’re looking for directly on the search results page and therefore have no reason to click through to any website.

Less than 1% of users get to the second page of search results

When users search for something, they don’t want to dig for the information they’re looking for. That’s why it’s no surprise that only 0.44% of Google search users visit second-page results 12 . To succeed online, it’s essential that your website ranks on page one for as many relevant keywords as possible.

The first organic result in a search results page has an average click-through rate of 27.6%

Click-through rate (CTR) shows how often users who see your organic search result actually click through to your web page. Page one results have an average click-through rate of 27.6%, compared to page two results, which have a much lower average rate of 15.8% 12 . The higher your web page ranks, the more clicks you’re likely to receive.

Building credibility is the main benefit of SEO

No matter what products or services you sell or how long you’ve been in business, credibility is important 6 .  If users trust your brand, they’ll feel more confident buying from you. A strong SEO strategy can also position you as a trustworthy leader in your industry and help you stand out from your competitors.

SEOs say their biggest challenges are lack of resources and strategy issues

It’s not easy to succeed in SEO, especially if you’re struggling with your strategy and/or don’t have the resources to design and implement it. Fortunately, you can take SEO courses to expand your knowledge and skills. Another option is to outsource SEO to the pros so you can focus on other, big-picture tasks, such as growing your business.

SEOs say that the biggest shifts in the industry are AI and Google updates

When asked about the “biggest shifts” and industry changes in SEO, 18.7% of respondents said machine learning and artificial intelligence while 18% said Google updates 1 . Since SEO is ever-evolving, it’s important to stay up to date on the latest changes so you can adjust your strategy accordingly.

Google’s algorithm has over 200 factors

Google considers more than 200 signals or clues when it determines its search engine rankings 10 . Penguin, which was created in 2012, is one of these signals. It helps reduce the presence of sites that use black hat SEO techniques. Other factors include domain age, keyword usage, content length, duplicate content, grammar and mobile usability.

Over 90% of searches worldwide happen on Google

While Google is not the only search engine out there, it’s where you should focus your SEO efforts as it has 90.68% of the search engine market share 17 . Once you’re pleased with how you’re ranking in Google, you can expand your efforts to other search engines, such as Bing, Yahoo, Naver and DuckDuckGo.

Google also owns almost 94% of market share for mobile search

When it comes to mobile search market share, Google takes the cake as well. Out of all the mobile searches that occur, 93.77% are on Google 17 . This reinforces the importance of ensuring your website is SEO-friendly on mobile devices. It should look great and be easy to navigate on iOS and Android.

The second and third most used search engines worldwide are Yahoo and Bing with 3.23% and 3.17% market shares, respectively

Google is the most popular search engine and Yahoo and Bing are right behind it 17 . Since Google is blocked in some countries such as China, it might make sense to focus on Yahoo and Bing as well. Just keep in mind that these search engines have different algorithms than Google so your strategy might need to change.

In the past five years, Google has done approximately 36 major algorithm updates

Google updates its algorithm often to improve search quality and keep digital marketers on their toes. In fact, the search engine completed 36 algorithms between September 2018 and September 2023 18 . Thirteen of these updates were considered core updates. As a business owner who uses SEO, it’s your job to familiarize yourself with these updates as they come out.

The average internet user conducts three to four searches per day on search engines

Searching on Google is an everyday activity, just like taking a shower and brushing your teeth. On average, a Google user performs three to four searches per day 12 . It’s how they get answers to their questions and find solutions to their problems.

The most Google traffic comes from the United States, India and Brazil

People from around the world depend on Google search. Research shows that 27.03% of its traffic comes from the United States, followed by India (4.46%) and Brazil (4.41%) 2 . If you sell to customers abroad, make sure most of your content targets the U.S. market.

Google has over 8.5 billion searches per day worldwide

Believe it or not, Google runs around 99,000 search queries per second. This adds up to 8.5 billion searches per day in every part of the world 8 . If your website isn’t optimized for organic Google search, you’re missing out and need to design an SEO strategy as soon as possible.

About 95% of keywords have a volume of 10 or less searches per month

Keywords are words or phrases users type into search engines to find the content they’re looking for. Not all keywords are popular as 95% of all keywords have 10 or fewer searches per month 13 . A keyword research tool can give you some insight on what these keywords are in your industry so you know where to focus your SEO efforts.

15% of searches are brand new searches that have never been searched before by Google

You can find out which relevant keywords were searched for in the past. However, 15% of searches that Google sees every day are brand new 10 . This means the search engine constantly has to work to ensure users receive the best content for their queries.

There are more than 19,000 keywords with over 100,000 monthly search volume in the U.S.

There are 19,881 high-volume keywords with more than 100,000 monthly searches in the U.S 13 . You should identify what these keywords are so you can tailor your content around them. However, don’t forget to target uncommon or rare keywords as well. While higher-volume keywords can attract a wider audience, lower-volume keywords are easier to rank for.

Google’s Keyword Planner can overestimate keyword volumes by as much as 91%

Google Keyword Planner is a free tool you can use to learn about keywords related to your business and their search volumes. If you do take advantage of it, keep in mind that the search volumes it reveals are not 100% accurate. The tool can overestimate keyword impressions by up to 91% 19 .

14.1% of all keywords are phrased as a question

It’s not uncommon for users to use words such as “how,” “what,” “where” and “who” while searching as 14% of all keywords are questions 12 . By using questions in your content, you can build trust and draw in highly relevant traffic. When you create content for question keywords, keep things clear and concise, use bullet points and numbers, and add supportive images.

The most searched keywords on Google are ‘youtube,’ ‘facebook’ and ‘whatsapp web’

Social media and communication sites including “YouTube,” “Facebook” and “WhatsApp Web” are the most popular keywords users search for in Google 6 . Their volumes are 1,200,000,000, 867,000,000 and 543,300,000, respectively. This highlights the power of using social media in your digital marketing strategy.

According to experts, the most important ranking factors in 2023 are quality content, backlinks and search intent/relevance

SEO experts believe that the keys to ranking high in search engines in 2023 are quality content, backlinks and search intent or relevance 20 . Therefore, you should prioritize these factors as you develop and implement your SEO strategy.

Google rewrites the title 61% of the time while rewriting the description about 63% of the time

Each web page has a meta title and meta description that inform search engines what they’re about. The titles and descriptions you write are not set in stone as Google rewrites 61% of titles 13 and 62.78% of descriptions 1 . To write quality metadata, use the right keywords and be conversational.

The most common types of featured snippets are paragraphs (82%) and lists (11%)

Featured snippets are short excerpts of text that show up at the top of Google’s search results to quickly answer a user’s question. Most of these snippets are in paragraph form (81.95%) while the rest are in list form (10.77%) 1 . To increase your chances of ranking for a featured snippet, write content that answers questions, add a table of contents with anchor links and insert an FAQ section.

The average ‘age’ of top result pages is 2.6 years old

Unfortunately, high search engine rankings don’t happen overnight. On average, pages that are at the top of search results are 2.6 years old 13 . With patience and hard work, you can slowly but surely watch your pages jump up in rankings.

Click-through rates decline at an average of 32.3% for each position on the first page

There’s no denying that earning a spot at the top of search engine results takes hard work. However, this hard work is often worth it when you consider that the higher you rank, the more likely users are to click through to your web page. The average click-through rate for the number one spot is 27%, compared to the 2.4% click-through rate at the number 10 spot 12 .

Over 90% of pages never get organic traffic

In a perfect world, all of your pages would rank organically. The reality, however, is that more than 90% of pages never do 13 . To make sure your pages are not part of this statistic, create original, high-quality content, build backlinks to authoritative websites, and ensure they’re indexed.

The top-ranked search results typically have 3.8 times more backlinks than lower-ranked results

Backlinks are essentially votes that tell Google your web page is valuable and informative. They’re important to build as the top-ranked search results usually have 3.8 more backlinks than results in spots two through 10 12 . To build backlinks, reclaim unlinked mentions, try to earn a spot on “Best X” lists, reach out to journalists, become a source for other publishers and update old content.

About 95% of pages have no backlinks at all

Despite the fact that backlinks are essential if you want your pages to rank well, 95% of all web pages don’t have them 12 . Be a part of the 5% of pages with high-quality backlinks that lead to excellent search engine rankings. If you don’t have the time or knowledge to do so, don’t be afraid to consult an SEO professional.

In 2022, backlinks had an average cost of $361.44

Top-quality backlinks from highly authoritative sites with high domain authorities (DAs) do not come cheap. In 2022, they were an average of $361.44 13 . The price you might pay for a backlink will likely depend on DA, content quality, domain quality, top relevance, organic traffic and the country or language.

Long-form content receives 77% more links than short-form content

It can take a lot more time and effort to write a long blog post than a short blog post. However, the extra time commitment can pay off. Data shows that longer content with 3,000 or more words received 77.2% more links than shorter content with 1,000 or fewer words 12 .

The top link-building strategy in 2022 according to experts was to publish link-worthy content

Content is king, especially if you want to build quality backlinks that improve your SEO. Experts in the field believed that in 2022, content was the key to link-building success 1 . The content you create should be relevant, engaging and useful to your particular target audience. Don’t forget to make sure it’s 100% original.

26% of searches include a video in the results

According to research from BrightEdge, 26% of search results have a video thumbnail. 21 If you’re wondering if it’s worth it to add videos to your web pages, the answer is likely “yes.” Videos can help you stand out and get noticed by Google.

Videos have a 41% higher CTR than plain text results

Video content can make it easier and faster for your audience to get the information they’re looking for. That’s why it’s no surprise videos have a 41% higher click-through rate than text-filled pages 22 . If you’ve done everything you can but your page isn’t ranking as well as you’d like it to, add a video.

Mobile users are 12.5 times more likely to see organic image results and three times more likely to see organic video results

If you’re investing in videos and images for your website, make sure they’re optimized for mobile. Here’s why: mobile users saw 12.5 times more images and three times more videos in organic search 6 .

HD videos rank higher than lower-quality videos

High-definition, or HD, videos offer higher resolution and quality than standard-definition videos. Therefore, YouTube chose to highlight them 18 . Not only will poor-quality videos frustrate your users, they can also lead to lost views and subscribers. In addition, you may receive dislikes. If possible, put in the extra effort and turn your videos into HD videos.

63% of searches are conducted on a mobile device in the U.S.

Mobile devices made up 63% of organic search engine visits in the U.S., as of the fourth quarter of 2021 2 . To make sure your website is optimized for mobile search, use a responsive design, simplify your navigation, eliminate pop-ups and keep all content short and concise. Don’t forget to improve page speed as well.

58% of searches are conducted on a mobile device, globally

If you serve customers abroad, you should know that 58.33% of global organic search visits were conducted on mobile devices, as of the first quarter of 2023 2 . Therefore, it’s important that your website accommodates mobile search as well as an international audience.

Mobile users conduct about 4.96 billion searches per day worldwide

Every day, there are 4.96 billion mobile searches performed across the globe 2 . When you design your website for the global mobile market, remember to limit scrolling, add large, mobile-friendly buttons and ensure a clean, efficient design.

57% of local searches come from a mobile device and tablet

When users are out and about looking for a product or service in their local area, they’re likely to use their mobile devices to find it. In fact, 57% of all local searches are made on mobile devices and tablets 3 . This means your website should be compatible on mobile devices and tailor to local audiences.

Appearing first for a mobile search doesn’t mean you’ll appear in first position on a desktop search

It can be exciting to earn the number one spot on mobile search engine results pages (SERPs). However, that doesn’t mean you’ll be number one on desktop SERPs as well. Only 17% of websites kept their positions across both mobile and desktop SERPs. In addition, 37% lost their spots when searches came from mobile 6 .

20% of Google App searches are conducted by voice

Voice search allows users to speak to a mobile or desktop device to conduct a Google search. It can come in handy when they have their hands full or are on the go. Of all the searches in the Google App, 20% of them are performed by voice 10 .

50% of U.S. consumers use voice search every day

These days, voice search is more popular than ever before. In fact, half of all U.S. customers use it on a daily basis 4 . To ensure your website supports voice search and captures this audience, target question keywords and other long-tailed keywords, prioritize local SEO, use schema makeup and optimize speed.

Over 1 billion voice searches are conducted every month

Users perform over 1 billion voice searches every month 6 . If you’re not investing in voice search optimization, you’re missing out. As you tweak your website to meet the needs of those who use voice search, think about your audience’s voice search behavior and how you can create content that aligns with their intent.

Voice searches are longer than text-based searches, on average

It’s easier to say a long word or phrase than to type it. That’s why voice searches tend to be longer than text-based searches 8 . By using long-tailed keywords that are relevant to your business and target audience, you can cater to voice search users.

70% of voice search results pull from ‘Featured Snippets’ and ‘People Also Ask’

While Featured Snippets are highlighted text excerpts that appear at the top of a Google search results page, People Also Ask is a feature that shows users additional content and answers related to their search query. Featured Snippets and People Also Ask account for 70% of voice search results 6 .

Experts forecast that by 2024, voice search queries will increase to 2 billion per month

There’s a good chance voice search will continue to grow in popularity. SEO experts predict that the number of monthly voice search queries will increase from 1 billion to 2 billion in 2024. If you haven’t optimized your site for voice, now is the time to do so 24 .

The top voice assistants are Amazon, Google and Apple, with 69%, 25% and 5% market share in 2021, respectively

Voice assistants are programs that allow users to perform voice searches. As of June 2021, the leading voice assistant is Amazon (69%), followed by Google (25%) and Apple (5%). As long as your site accommodates voice search, it can reach users, no matter which voice assistant they choose to use 7 .

46% of all Google searches are local

Local SEO targets customers in a specific city, region or neighborhood. When you consider the fact that almost half of all Google searches have local intent, you’ll want to ensure local SEO is part of your overall SEO strategy, especially if you sell to customers in certain geographic areas 8 .

50% of smartphone users visited a store within a day of their local search

If your goal is to get customers in the door as soon as possible, local search is essential. Half of all smartphone users who perform a local search went to a store they found through it within a day 10 .

Four in five people use search engines for local queries

The popularity of local queries is significant. Four out of five people use search engines to meet a need they have in their local area 10 . To optimize your website for local search, claim your Google Business Profile listing, add your business to local directories, create content with local search terms and add schema markup.

‘Open now near me’ searches have increased by 400%

Customers don’t want to wait long to go to a business and buy what they need. That’s why they often search “open now near me.” These searches have gone up by 400% from September 2019 to August 2020 and September 2020 to August 2021 10 . By claiming your Google Business Profile listing and adding your hours, you can take advantage of this trend.

18% of local searches lead to a purchase within 24 hours

If you’d like to convert users while your product or service is fresh in their minds, local search is key. Compared to 7% of non-local searches, 18% of local smartphone searches resulted in a purchase within a day 10 .

42% of local searchers click into map pack results

Google Maps Pack is a set of three Google Maps search results that usually appear when users perform local searches for businesses. Earning a spot on the Google Maps Pack can do wonders for your business as 42% of local searchers click on these results 12 . To increase your chances of becoming a Google Maps Pack result, claim your Google Business Profile listing, generate more online reviews and build citations.

Almost 64% of customers are likely to read Google reviews before visiting a local business

Don’t underestimate the power of Google reviews as 63.6% of customers will read them before they stop into a local business 11 . To get more Google reviews, provide excellent customer service, add review links to your website and emails, and simply ask for reviews from current and former customers.

The average SEO budget for a small business is almost $500 per month

You don’t have to spend thousands upon thousands of dollars on SEO. On average, a small business budget is $497.16 per month 12 . You can always increase your budget as you see results and earn more revenue.

SEO agencies charge an average of over $3,200 per month

If you don’t have the in-house time or expertise to focus on SEO, you might consider an agency. However, keep in mind that SEO agencies can be pricey. You can expect to pay an average of $3,209 per month for their services 13 .

SEO roles are projected to grow by 22% between 2020 and 2030

SEO specialists can work for digital marketing agencies or in-house marketing departments. The demand for these types of professionals is forecasted to increase by 22% between 2020 and 2030 14 . Depending on your budget and goals, you might want to hire an SEO specialist to assist with your SEO strategy.

The SEO industry is forecasted to be worth almost $218 billion by 2030

Rest assured SEO isn’t going anywhere anytime soon. By 2030, the SEO services market is predicted to grow to $217,846 15 . As a small business, SEO should be a part of your overall marketing plan today, tomorrow and years down the road.

52% of business leaders develop content with the help of AI

Content marketing, which focuses on creating useful and informative content can lead to better SEO results. Thanks to the rise in AI content generation tools, you can simplify your content marketing efforts, just like 52% of business leaders who are currently doing so 16 .

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Anna Baluch is a freelance writer from Cleveland, Ohio. She enjoys writing about a variety of health and personal finance topics. When she's away from her laptop, she can be found working out, trying new restaurants, and spending time with her family. Connect to her on LinkedIn.