Artificial Intelligence…Is Intelligent!

We are at a defining moment – like the internet in the ‘90s – where Artificial Intelligence (AI) is moving toward mass adoption.


We are entering the fifth industrial revolution with Artificial Intelligence (AI). More data is created per hour today than in an entire year just two decades ago and global data is expected to double every two years. The data we are creating is increasing exponentially and we need AI to analyze and interpret it. New applications are being developed constantly that require more data. Holograms, metaverse, brain computer interfaces and EVTOL (electric vertical take-off and landing) are just some of the technologies that will be very data-heavy.


AI leverages large data sets and uses algorithms to find underlying relationships, which can be used to drive new or better business outcomes. Recently, there has been tremendous focus on generative AI, which is AI that can generate output based on the data on which they have been trained. Some of the key AI technologies include machine learning, deep learning, predictive analytics, natural language processing and machine vision. Algorithms can be used to carry out machine and deep learning. Linear regressions and logistic regressions are some examples of machine learning algorithms. Machine learning algorithms automatically apply mathematical calculations to big data to learn from past data to produce repeatable and reliable decisions and results, such as improved video suggestions based on past video viewing activity.


What can AI do?

ChatGPT is a chatbot that can generate coherent human-like text. It is the first application of its kind that is openly available to a wide audience. Until now AI could only read and write but could not understand content. Since ChatGPT can generate human-like text, it can be used for content generation (e.g., writing essays, news articles, social media posts, marketing content, stories, music, emails), data extraction, summarizing text, optimizing web browsers, language translation and computer programming. Generative AI models like ChatGPT enable machines to understand human language and consecutively produce human-like dialogue and content. Since its launch on November 30, 2022, ChatGPT has gained significant traction, amassing one million users after merely five days.

Daily unique visits to ChatGPT and cumulative TikTok downloads after their launches

AI, a game changer for the next decade of digital growth

The BofA Global Research U.S. Software Technology team believes AI technologies can be embedded across industries and simplified for the average user which could drive adoption over the next decade. They expect stock performance for large tech companies to be tied to their AI development, estimating AI/ML (machine learning) capex investment to exceed USD$40 bn. This investment would spur revenues leading to the global AI market reaching USD$900 bn by 2026. In addition, the BofA Global Research U.S. Semiconductor team anticipates continued AI investments to be medium- to long-term tailwinds for cloud semiconductor vendors given the substantial amount of computer processing necessary.


Sectors using text data are likely to incorporate this tech to increase productivity

Any industry that uses text data might be affected by this technology. For example, call centers, legal searches, document writing, authoring, jobs involving spreadsheets, insurance and healthcare are areas that involve a large amount of text data.


It is important to bear in mind that AI and machine learning are enabling technologies. AI must be coupled with something to be useful. Hence, while these advanced language models could be disruptive to these sectors in terms of replacing jobs that involve relatively routine tasks, jobs in these sectors will not disappear altogether. They are likely to incorporate AI, allowing for greater productivity potential.

Search engines could become more conversational, embedded with language models. This would be a new user interface within search engines rather than a disruption of the search engine market in general.


This technology is likely to evolve beyond single applications (e.g., text and images) toward multimodality – for instance, using text, images, voice recordings as prompts to generate a response from the AI system. More proficient language model deployment could proliferate conversational tools into such things as word processors, virtual video meetings and email systems to enable their onboarding for more users to interact via speech. Another application is that this technology could be used to generate entire programming applications rather than just being able to suggest or explain code.


Sectors that can combine computing power, data and talent to enable AI could capitalize on the commercial opportunities. Operating costs (e.g., semiconductors, staff) could present a large barrier to entry. As the parameter size increases, costs increase too. This could be challenging to absorb when such models perform billions of queries a day. For this technology to be more viable, we would likely need a 10x-to-20x improvement in efficiency, otherwise it would be too costly for entrants to deploy them commercially. But falling hardware costs for larger companies could potentially alleviate this issue.


Across the board, there are GDP (gross domestic product) gains associated with productivity and product enhancements. Gains in the services industry, which includes health, education, public services and recreation, could add 21% to GDP by 2030. This is mainly due to the healthcare sector, which should see greater personalization and quality improvement in medical advice. Healthcare professionals could improve the patient experience by using virtual assistants and camera-based healthcare apps in diagnosing medical conditions. Transport and logistics, and financial and professional services, are estimated to see smaller GDP gains of 10% each due to AI.


A $15 trillion market by 2030

AI could contribute up to USD$15.7 tn to the global economy by 2030,1 while open data (data that anyone can access, use and share) has the potential to unlock USD$3.2 tn to USD$5.4 tn in economic value annually via, for example, reducing emissions, increasing productivity and improving healthcare.2 According to IDC, global revenues for the AI market, including software, hardware and service sales, will grow at a CAGR (compound annual growth rate) (2022E-26E) of 19% to reach USD$900 bn by 2026.3 Big Data and AI could double the gross value-added (GVA) growth rates of developed markets by 2035 (estimated) and add 0.8 ppt–1.4 ppt to global productivity growth in the long run.

Global AI market size (US$ bn)

According to Accenture, AI could double annual global economic growth rates by 2035. AI is likely to drive this in three different ways. Firstly, AI will lead to a strong increase in labor productivity (by up to 40%) due to automation. Secondly, AI will be capable of solving problems and self-learning. Thirdly, the economy will benefit from the diffusion of innovation.


North America and China could see the biggest economic gain in percentage terms from AI. PwC estimates that AI will enhance GDP by 26.1% in China and 14.5% in North America in 2030, which accounts for c.70% of the global impact.1 Due to the fact that North America has advanced technological and consumer readiness for AI, which enables a faster effect of AI on productivity and overall, a larger effect by 2030 and for China, productivity and product enhancements GDP effects are higher than in other regions.


A supportive investment landscape for AI

AI adoption has more than doubled over the past five years with investment in AI increasing quickly: Funding of generative AI increased by 71.4% year over year in 2022.4 In particular, global private investment in AI increased 48% year over year in 2021 to USD$93.5 bn, more than double the total private investment in 2020.5

Global corporate investment in AI by activity type, 2013-21 (US$ bn)

Risks of the robots

COVID has hastened the adoption of technologies such as AI, chatbots, robot process automation (RPA) in white collar roles and industrial robots in blue collar jobs – all of which we estimate could displace 2 billion jobs by 2030. Up to 47% of U.S. jobs could impacted from computerization as less time is spent on routine and manual tasks that require time in training and education. For example, the Transport and Logistics sector could have relatively more day-to-day tasks that can be automated (e.g., conducting document checks at customs to ensure a smoother process).

Likelihood of job automation (today vs next 20 years) where red bars denote jobs likely to be automated and green bars denote those likely to remain

North America and Europe more at risk of automation

By region, jobs in North America and Europe are more at risk of automation (averaging c.43% and c.35% across different sectors, respectively), whereas countries in Asia have a much lower potential. This can be explained by the industry composition and differences in how regions perform the same job function. Countries with a greater focus on manufacturing have a higher risk of automation.


Machines have their limits: premium for jobs with intelligence and creativity

Workers should look toward tasks with skill sets that robots and computers cannot easily accomplish in the next 10 to 20 years. For instance, dexterity is something that current robot hardware technologies have not yet mastered. There will likely be an increasing premium for jobs that require social intelligence, creativity and complex problem-solving.


New jobs are likely to come from health, STEM (science, technology, engineering and mathematics) and managerial roles

AI could also create new jobs, particularly with regard to training and maintaining the AI technology. The net result on the labor market depends on the number of jobs created versus the number that are automated.


Job market: automation

Since ChatGPT can generate human-like content, it is possible to automate tasks, hence displacing certain tasks. It could impact the Advertising, Art and Design and Entertainment sectors. Health and Science are least likely to experience automation but Manufacturing and Transportation are the most likely.


Travel and Transportation are some of the industries which could see the greatest potential incremental value from AI. In contrast, the more science-based sectors like Aerospace and Defense, Semiconductors, Pharmaceuticals and Healthcare could see relatively some of the lowest potential incremental values from AI. Hence, these sectors are less likely to experience job displacement by ChatGPT or similar generative AI programs.


Don’t underestimate humans’ ability to one-up technology

There are areas where humans can beat machines. In the future, we believe there will likely be an increasing premium for jobs within occupational groups that require social intelligence, creativity and complex problem solving as opposed to repetitive, low-dexterity skills. For instance, an event planner requires more social intelligence than a dishwasher in hospitality, fashion designers require more creativity than a seamstress in apparel, and a medical surgeon requires greater perception and manipulation of tasks than a clinic receptionist within healthcare. The mass adoption of AI can usher society into the fifth industrial revolution. We are at a defining moment – like the internet in the ‘90s – where AI is moving toward mass adoption, with large language models like ChatGPT finally enabling us to fully capitalize on the data revolution.

Data Sources:

1 PwC (PricewaterhouseCoopers) Did You Know: Artificial Intelligence to drive GDP gains of USD$15.7 tn. June 27, 2017.

2 McKinsey Open data: Unlocking innovation and performance with liquid information. October 2013.

3 IDC (International Data Corporation) Forecasts 18.6% Compound Annual Growth for the Artificial Intelligence Market in 2022–2026.

4 McKinsey: The state of AI in 2022—and a half decade in review. December 2022.

5 Stanford University Human-Centered Artificial Intelligence Index Report 2022.


Read our full analysis for a more in-depth look at these trends.