6 Charts That Explain Why the Machine Learning & AI Job Market is on Fire
As we enter 2022, the AI job market is on fire. Data science, machine learning, data engineering, and similar AI roles continue to occupy the top spot by many measures, such as salary, desirability, and job prospects. This is all down to demand lead growth. Not only is the AI job market on fire but we believe it will continue to burn brightly. Here’s a look at 6 charts that explain why the AI job market is doing so well.
Chart #1: $83.7 Billion in Venture Capital Funding
Chart 1 from Pitchbook shows AI & ML investing was at an all-time high in 2021. Globally venture capitalists closed 4,021 AI deals with a record amount of funding of $83.7 billion. For startups, the hard work begins once the funds are wired and they need to put the money to work. AI startups spend much of the first and subsequent round funds on engineering and AI/ML talent. Thus, they need to hire the best in the field at an unprecedented pace to ensure success and to get to their next funding round. Additionally, in 2021 although the number of investments dipped slightly, the amount raised per startup increased significantly. Flush with cash, many AI startups are offering unprecedented amounts to already well-compensated and accomplished AI & ML experts, which is helping raise salaries across the board.
For 2022, and the next 5 years, expect more of the same. Record investment and intense competition for top talent. Author’s note: Interested in AI startups? Check out ODSC East AI Start-up Showcase.
Chart Credit: Pitchbook 2021, Q3, Emerging Tech Report
Chart #2: Everybody’s Doing It
Not to be outdone by startups, corporations are weighing in heavily on AI & ML hiring. After years of handwavy hype around AI and related technologies, companies are finally buckling down and doing the hard work of reshaping their businesses to take advantage of AI. Chart 2 from PWC’s 2021 AI report nicely illustrates that: 58% of companies from their representative survey are fully committed to AI & ML. Fully 93% are committed to the AI path, and only a very small percentage of 7% have no plans to do so. That’s an astonishing uptick for a technology that first came on their radar less than 5 years ago.
This chart also illustrates that there is a lot of AI implementation capacity to fill. Removing the 7% with no interest (thus far) and the 25% fully implementing AI still leaves 68% that need to fully engage AI. Much of this will come from new hiring. Thus we expect significant growth in the AI & ML job market over the next decade.
Chart Credit: PWC 2021 AI Survey Report
Chart #3: AI is Eating Software at a Rate Greater Than Moore’s Law
Thanks to Moore’s Law, which predicted computing power will double every two years, software, as Marc Andreessen famously stated, is eating the world. But that was back in 2011 before he knew there was a bigger fish lurking in the pond. According to this interesting chart from chip designer Synopsys, AI-driven software is doubling every 3 to 4 months. The left side of the chart shows the steady progression of Moore’s law. But it pales in comparison to the right side, which shows exponential growth beginning approximately the same time as the Alexnet competition breakthrough in 2012 up to to AplhaGoZero in 2020 (since surpassed).
It’s evident that AI is the bigger fish in the software industry, so expect every aspect of software, from design, development, and deployment, to be consumed by AI over the next decade and beyond. This will lead not only to the reskilling of software and hardware engineering roles, but also the creation of hybrid roles, new roles (see chart 5), and generally increased demand for AI & ML experts.
Above: Chip design through the ages: Now it’s AI’s turn. Chart Credit: Synopsys AI is Eating Software
Chart #4: A Bigger Slice of the Emerging Tech Sector
AI is not the only new(ish) kid on the block, but pundits like to put it in the emerging tech sector. Emerging tech is set to be the driving force for the global economy over the next decade. It’s a long list that includes everything from clean energy and autonomous machines to crypto and quantum computing. Many of these jobs will incorporate aspects of other technologies including AI. For example, there is significant excitement around quantum machine learning and AI-enabled cybersecurity. Chart 4 shows data from the CompTIA recent jobs market report. The emerging tech sector is huge, but AI & ML is taking a very respectable 12.6% (and growing) slice of that.
The net result of this is increased demand for AI & ML in the jobs market. As emerging tech continues to take a bigger slice of the manufacturing and service economy, it will draw in AI & ML experts from around the world.
Chart Data Source CompTIA Tech Jobs Report, December 2021
Chart # 5: ~54,320 Papers on ML & AI Related Topics in 2021
ODSC wasn’t the first data scientist conference when we started back in 2013 (as the Boston Data Festival). Many excellent academic conferences in AI have been around for decades. Although ODSC’s content is more focused on applied and open source technologies, over the last 8 years we’ve kept a very close eye on new AI developments that make the move from academia to the real world. Much of the advancement in AI & ML emerged from either academia or open-source, oftentimes both. A good proxy of what’s to come in the industry can be gauged from research papers published in the field. Chart 5 illustrates data from arXiv.org for papers published in AI, ML, and related topics (computer vision, etc). Between 2018 and 2021 papers published in this field have more than doubled.
Overall we expect academic/institutional research to continue to accelerate, driving new job growth over the next few decades. Note the new fields of AI emerging from research, such as differential privacy, machine learning safety, meta machine learning, just to name a few. These new techniques will drive the industry to new growth as AI becomes more ubiquitous and powerful.
(authors note: if you’re interested in AI research, check out ODSC East’s focus areas — Research Frontiers, Machine Learning Safety, and Responsible AI)
Chart #6: Less Sexy But More Desirable
We’ve come a long way since 2012 when data science was hailed as the sexiest job of the 21st century. Many new roles (or relabeling of existent roles) have since emerged. Data scientist still holds one of the top spots, but machine learning engineer, data engineers, MLOps engineer, and AI engineer are starting to catch up. Chart 6 from Dice’s 2020 jobs report nicely illustrates these trends.The role of data engineering has been around for quite a while (in other guises), but thanks to the voracious data appetite of AI & ML the role has really taken off. The same can be said for the role of machine learning engineer.
Few expect job roles to be stagnant. New roles are quickly emerging that are just as desirable, such as MLOps Engineer, responsible for deploying and maintaining production models, Machine Learning QA/Test Engineer, responsible for embedded models in autonomous systems, and AI Ethicist, responsible for the ethical dimension of AI and machine learning. AI Technical Writer, AI Project Manager, and AI Data Developer are a few more examples of expanding new job roles that will create new desirable positions and demand in the field.
Chart Credit: Dice 2020 Tech Jobs Report
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