I acknowledge the Gadigal people of the Eora nation, the traditional owners of these lands, and pay respect to all First Nations people present.
Barely a day goes by without someone discovering a new use for artificial intelligence. Financial institutions are using AI to detect fraud, by looking for unusual transaction patterns. AI integrated with virtual reality is being used to create highly realistic training simulations for pilots, first responders and surgeons. Musicians are using AI to create new instruments and vocal processes. Educators are using AI to personalise the learning experience. Dating coaches are using AI to train people on finding their perfect match. Gardeners are using AI to choose which plants will work best together, schedule optimal watering times and devise pest control strategies. Carers are using AI to craft fictional stories that are perfectly tailored for young listeners.
AI engines have matched and exceeded humans on a range of tests. As Stanford University’s AI Index 2024 Annual Report points out, artificial intelligence has exceeded human benchmarks on tasks such as reading comprehension, image classification and visual reasoning (see Figure 1). As AI has surpassed these benchmarks, researchers have had to identify new challenges, such as competition‑level mathematics, where AI has moved from 10 per cent of human‑level performance in 2021 to 90 per cent on the latest estimates (Maslej et al 2024).
Public interest in AI is growing accordingly. Figure 2 shows the monthly volume of internet searches in Australia for the phrase ‘artificial intelligence’. In the months following the release of ChatGPT on 30 November 2022, searches for AI tripled, and remain high.
There is considerable interest in the effect of AI and related technologies on the labour market. Some research considers how AI might change the nature of work. For example, Dell’Acqua et al (2023) estimate the impact of AI on the productivity of management consultants by randomly assigning consultants to undertake mock tasks with or without the assistance of ChatGPT, and find large positive impacts of the technology on productivity. Other studies have discussed the likely impact of AI on work with reference to prior technological shocks (see for example Coelli and Borland 2023).
One possible way that AI may affect the market is through increased demand for AI‑related skills — that is, skills that enable people to develop or work with AI models. Recent work by OECD researchers (Borgonovi, et al. 2023) examined demand for these skills by searching the full text of job advertisements for AI‑related keywords. The study, which includes Australia, relies on data from Lightcast, a company specialising in labour market data that was formed from a merger between Emsi and Burning Glass Technologies.
For Australia, the Lightcast data are extensive, but are not the most comprehensive source. From 2019 to 2022, Lightcast’s database included 1.1 to 1.3 million unique Australian job advertisements (Borgonovi, et al. 2023, p.51). By contrast, we use data from SEEK, Australia’s largest online employment marketplace. Although SEEK does not disclose the number of jobs in its database, we can confirm that its coverage for those years was around twice as large as Lightcast. Another advantage of SEEK data over the Lightcast data is that the former has extensive data on the Australian labour market, including data that is not always displayed publicly, such as the advertised salary for each role.
What constitutes an AI job? We opt to follow the approach of Borgonovi et al. (2023), which involves searching the text of job ads for a range of skill‑related keywords, such as ‘AI ops’ or ‘PyTorch’. Each of these skills are classed by the authors as either ‘generic’ or ‘specific’. If a job advertisement contains at least 2 generic or one specific AI skills, it is classified as an ‘AI job’. A full list of these skills is provided in the Appendix.
This approach to identifying AI jobs is relatively straightforward, but is also somewhat conservative — advertisements for AI jobs that do not explicitly mention AI‑related skills and terms in the text of the ad will fail to be identified as AI jobs in this methodology.
Note too that our analysis does not aim to identify any roles that have been displaced or negatively affected by the emergence of AI technology. Instead, our focus is on estimating the share of new jobs in the Australian economy that require AI skills.
We begin by applying this methodology to the SEEK dataset. Figure 3 shows the share of Australian job advertisements that are ‘AI jobs’, on a monthly basis over the period from 2017 to 2024. In 2017, just 0.06 per cent of advertisements are for AI jobs. This figure peaked at 0.2 per cent in 2021, before declining slightly to 0.17 per cent in early‑2024.
There are 2 clear takeaways from these results. First, demand for AI skills has clearly grown substantially, approximately tripling since 2017. Second, AI jobs are extremely rare. Using Lightcast data, Borgonovi et al. (2023) find that AI jobs constitute less than 1 per cent of all advertised openings for each of the 14 countries in their sample. Nonetheless, they estimate figures that are considerably higher than ours. In 2022, Lightcast data suggest that AI jobs (defined in the same way as in our study) comprised 0.84 per cent of job advertisements in the United States, 0.54 per cent in Canada, and 0.51 per cent in the United Kingdom.
Part of the difference in our results may be related to data differences. For Australia, the Lightcast figures suggest that 0.4 per cent of 2022 employment postings were for AI jobs, around twice as large as our estimate for the same year. Given that our dataset is considerably larger, we are inclined to prefer our estimate, but it is plausible that that the Lightcast dataset has some advantages that we have not considered.
One possible theory for the declining proportion of AI jobs in the latter years of our sample is that our keywords are better at capturing the nature of AI jobs in 2017 than in 2024. However, this does not appear to be the whole story. Figure 4 plots the share of job advertisements on SEEK that simply include the phrase ‘artificial intelligence’. This too rises from 2017 to 2022, before declining slightly. The share of job advertisements that mention ‘artificial intelligence’ rose 6‑fold from 2017 to 2022, but fell by one‑third from 2022 to 2024. At the end of the period we analyse, around 1 in 1000 Australian job postings on SEEK include the phrase ‘artificial intelligence’.
Next, we turn to examine which occupations feature the largest proportion of AI jobs, using data from the 12 months to March 2024 (inclusive). Occupations are defined at the 4‑digit Australian and New Zealand Standard Classification of Occupations (ANZSCO) level. These results are shown in Figure 4.
We find that the occupation that tops the list is ‘Mathematical Science Professionals’, a group that includes Data Scientists. Within this occupation, 6.3 per cent of ads for Mathematical Science Professionals are AI jobs. The rest of the top 10 list is dominated by other science, technology, and research roles, all of which have 1 per cent or more of their positions as AI jobs. Some may be surprised to learn that ‘Nurse educators and researchers’, ‘Music professionals’ and ‘Social professionals’ are among the top 10 occupations for their share of AI jobs.
Another way of analysing the data is by industry. SEEK’s data is not collected using the Australian and New Zealand Standard Classification of Industries. Instead, employers are asked to select from one of 29 pre‑defined ‘classifications’ for their advertised roles.
As Figure 5 shows, the classifications in which AI jobs are most prevalent are Science & Technology (2.2 per cent) and Information & Communication Technology (1.23 per cent). At the other end of the spectrum, not a single job ad in the Retail & Consumer Products classification in the year to March 2024 met the ‘AI job’ criteria.
Do AI jobs pay more? In the 12 months to March 2024, the average advertised salary for AI jobs was $121,275. This is 31 per cent higher than the average for non‑AI jobs. However, much of this difference reflects compositional differences — AI jobs are over‑represented in occupations that tend to pay well regardless of whether AI skills are mentioned in the job ad.
To control for these compositional issues, we run fixed effects regressions, controlling for occupation and state effects. We run these regressions separately for each year in the sample. In 2017, we estimate the pay premium for AI jobs was 11 per cent. Over the ensuing years, the pay gap between AI jobs and other jobs in the same state and occupational category steadily fell. In 2023 and 2024, the final years of our sample, the pay premium for AI jobs was just 4 per cent. Averaged across the entire period 2017 to 2024 (and controlling for state, time and occupation fixed effects), the pay premium for AI jobs was 6 per cent.
Conclusion
Notwithstanding the considerable public interest in generative artificial intelligence, AI jobs constitute a tiny share of all advertised positions. In 2024, AI jobs comprised only 0.17 per cent of job postings, meaning that just 1 in 588 advertised roles were for AI jobs. From 2022 to 2024, AI jobs declined as a share of job postings. Similarly, a simple search for the phrase ‘artificial intelligence’ in posted jobs shows that the share of such positions also dropped from 2022 to 2024. In 2024, only about 1 in 1000 advertised roles contained the phase ‘artificial intelligence’.
Across occupations, AI jobs are most prevalent in science, technology and research roles. The largest share of AI jobs are among Mathematical Science Professionals, where 6.3 per cent of vacant positions are AI jobs. Across industries, AI jobs are most prevalent in science and technology. AI jobs are least prevalent (indeed, non‑existent) in the retail and consumer products industry. In future work, we intend to compare trends in total employment in occupations with a high share of AI jobs with trends in employment in occupations with a low share of AI jobs.
Finally, we find that AI jobs pay higher wages than non‑AI jobs. This remains true even holding constant time, geography and occupation. Without controls, AI jobs pay 31 per cent more than non‑AI jobs. With controls, AI jobs pay 6 per cent more than non‑AI jobs. There is some evidence that the wage premium for AI jobs declined over the period 2017 to 2024.
References
Acemoglu D., Autor D., Hazell, J. and Restrepo, P. (2022), ‘Artificial Intelligence and Jobs: Evidence from Online Vacancies’, Journal of Labor Economics, 40(S1).
Borgonovi, F., Calvino, F., Criscuolo, C., Samek, L., Seitz, H., Nania, J., Nitschke, J and O’Kane, L. (2023), ‘Emerging trends in AI skill demand across 14 OECD countries’, OECD Artificial Intelligence Papers, No. 2, OECD Publishing, Paris.
Coelli, Michael Bernard and Borland, Jeff, (2023) ‘The Australian Labour Market and IT‑enabled Technological Change’, Melbourne Institute Working Paper No. 01/23, Melbourne Institute, Melbourne.
Dell’Acqua F, McFowland E, Mollick ER, Lifshitz‑Assaf H, Kellogg K, Rajendran S, Krayer L, Candelon F and Lakhani KR (2023) ‘Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality’, Harvard Business School Technology and Operations Management Unit Working Paper 24–013, Harvard Business School, Boston, MA.
Felten E, Raj M, Seamans R (2021) Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses. Strategic Management Journal 42(12):2195–2217.
Maslej, Nestor Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, and Jack Clark, (2024) The AI Index 2024 Annual Report, AI Index Steering Committee, Institute for Human‑Centered AI, Stanford University, Stanford, CA.
Appendix: Categorisation of AI Skills
Following Borgonovi et al (2023), we classify a job posting as an ‘AI job’ if it contains at least 2 generic or one specific AI skill, from the list below.
| Skill | AI Skill Cluster | Category |
|---|---|---|
| AIOps (Artificial Intelligence For IT Operations) | Artificial Intelligence | Specific |
| Applications Of Artificial Intelligence | Artificial Intelligence | Generic |
| Artificial General Intelligence | Artificial Intelligence | Generic |
| Artificial Intelligence | Artificial Intelligence | Generic |
| Artificial Intelligence Development | Artificial Intelligence | Generic |
| Artificial Intelligence Markup Language (AIML) | Artificial Intelligence | Specific |
| Artificial Intelligence Systems | Artificial Intelligence | Generic |
| Azure Cognitive Services | Artificial Intelligence | Specific |
| Baidu | Artificial Intelligence | Generic |
| Cognitive Automation | Artificial Intelligence | Specific |
| Cognitive Computing | Artificial Intelligence | Specific |
| Computational Intelligence | Artificial Intelligence | Specific |
| Cortana | Artificial Intelligence | Generic |
| Expert Systems | Artificial Intelligence | Generic |
| Intelligent Control | Artificial Intelligence | Generic |
| Intelligent Systems | Artificial Intelligence | Generic |
| Interactive Kiosk | Artificial Intelligence | Generic |
| IPSoft Amelia | Artificial Intelligence | Specific |
| Knowledge‑Based Configuration | Artificial Intelligence | Generic |
| Knowledge‑Based Systems | Artificial Intelligence | Generic |
| Multi‑Agent Systems | Artificial Intelligence | Generic |
| Open Neural Network Exchange (ONNX) | Artificial Intelligence | Specific |
| OpenAI Gym | Artificial Intelligence | Specific |
| Reasoning Systems | Artificial Intelligence | Specific |
| Soft Computing | Artificial Intelligence | Generic |
| Syman | Artificial Intelligence | Generic |
| Watson Conversation | Artificial Intelligence | Generic |
| Watson Studio | Artificial Intelligence | Specific |
| Weka | Artificial Intelligence | Generic |
| Advanced Driver Assistance Systems | Autonomous Driving | Generic |
| Autonomous Cruise Control Systems | Autonomous Driving | Specific |
| Autonomous System | Autonomous Driving | Specific |
| Autonomous Vehicles | Autonomous Driving | Specific |
| Guidance Navigation And Control Systems | Autonomous Driving | Generic |
| Light Detection And Ranging (LiDAR) | Autonomous Driving | Generic |
| OpenCV | Autonomous Driving | Specific |
| Path Analysis | Autonomous Driving | Generic |
| Path Finding | Autonomous Driving | Generic |
| Remote Sensing | Autonomous Driving | Generic |
| Unmanned Aerial Systems (UAS) | Autonomous Driving | Generic |
| AdaBoost (Adaptive Boosting) | Machine Learning | Generic |
| Apache MADlib | Machine Learning | Specific |
| Apache Mahout | Machine Learning | Specific |
| Apache SINGA | Machine Learning | Generic |
| Apache Spark | Machine Learning | Generic |
| Association Rule Learning | Machine Learning | Specific |
| Automated Machine Learning | Machine Learning | Specific |
| Autonomic Computing | Machine Learning | Generic |
| AWS SageMaker | Machine Learning | Specific |
| Azure Machine Learning | Machine Learning | Specific |
| Boosting | Machine Learning | Generic |
| CHi‑Squared Automatic Interaction Detection (CHAID) | Machine Learning | Specific |
| Classification And Regression Tree (CART) | Machine Learning | Specific |
| Cluster Analysis | Machine Learning | Specific |
| Collaborative Filtering | Machine Learning | Specific |
| Confusion Matrix | Machine Learning | Generic |
| Cyber‑Physical Systems | Machine Learning | Generic |
| Dask (Software) | Machine Learning | Generic |
| Data Classification | Machine Learning | Generic |
| Dbscan | Machine Learning | Specific |
| Decision Models | Machine Learning | Specific |
| Decision Tree Learning | Machine Learning | Specific |
| Dimensionality Reduction | Machine Learning | Specific |
| Dlib (C++ Library) | Machine Learning | Specific |
| Ensemble Methods | Machine Learning | Specific |
| Evolutionary Programming | Machine Learning | Generic |
| Expectation Maximization Algorithm | Machine Learning | Specific |
| Feature Engineering | Machine Learning | Specific |
| Feature Extraction | Machine Learning | Specific |
| Feature Learning | Machine Learning | Specific |
| Feature Selection | Machine Learning | Generic |
| Gaussian Process | Machine Learning | Generic |
| Genetic Algorithm | Machine Learning | Specific |
| Google AutoML | Machine Learning | Specific |
| Google Cloud ML Engine | Machine Learning | Specific |
| Gradient Boosting | Machine Learning | Specific |
| H2O.ai | Machine Learning | Specific |
| Hidden Markov Model | Machine Learning | Generic |
| Hyperparameter Optimization | Machine Learning | Specific |
| Inference Engine | Machine Learning | Specific |
| K‑Means Clustering | Machine Learning | Specific |
| Kernel Methods | Machine Learning | Generic |
| Kubeflow | Machine Learning | Specific |
| LIBSVM | Machine Learning | Specific |
| Machine Learning | Machine Learning | Generic |
| Machine Learning Algorithms | Machine Learning | Generic |
| Markov Chain | Machine Learning | Generic |
| Matrix Factorization | Machine Learning | Generic |
| Meta Learning | Machine Learning | Generic |
| Microsoft Cognitive Toolkit (CNTK) | Machine Learning | Specific |
| MLflow | Machine Learning | Specific |
| MLOps (Machine Learning Operations) | Machine Learning | Specific |
| mlpack (C++ Library) | Machine Learning | Specific |
| Naive Bayes | Machine Learning | Generic |
| Perceptron | Machine Learning | Generic |
| Predictionio | Machine Learning | Specific |
| PyTorch (Machine Learning Library) | Machine Learning | Specific |
| Random Forest Algorithm | Machine Learning | Specific |
| Recommendation Engine | Machine Learning | Specific |
| Recommender Systems | Machine Learning | Specific |
| Reinforcement Learning | Machine Learning | Specific |
| Scikit‑learn (Machine Learning Library) | Machine Learning | Specific |
| Semi‑Supervised Learning | Machine Learning | Specific |
| Soft Computing | Machine Learning | Generic |
| Sorting Algorithm | Machine Learning | Specific |
| Supervised Learning | Machine Learning | Specific |
| Support Vector Machine | Machine Learning | Specific |
| Test Datasets | Machine Learning | Generic |
| Torch (Machine Learning) | Machine Learning | Generic |
| Training Datasets | Machine Learning | Generic |
| Transfer Learning | Machine Learning | Specific |
| Unsupervised Learning | Machine Learning | Specific |
| Vowpal Wabbit | Machine Learning | Specific |
| Xgboost | Machine Learning | Specific |
| Amazon Textract | Natural Language Processing | Specific |
| ANTLR | Natural Language Processing | Generic |
| BERT (NLP Model) | Natural Language Processing | Specific |
| Chatbot | Natural Language Processing | Generic |
| Computational Linguistics | Natural Language Processing | Generic |
| DeepSpeech | Natural Language Processing | Specific |
| Dialog Systems | Natural Language Processing | Generic |
| fastText | Natural Language Processing | Specific |
| Fuzzy Logic | Natural Language Processing | Generic |
| Handwriting Recognition | Natural Language Processing | Generic |
| Hugging Face (NLP Framework) | Natural Language Processing | Specific |
| Hugging Face Transformers | Natural Language Processing | Specific |
| Intelligent Agent | Natural Language Processing | Generic |
| Intelligent Software Assistant | Natural Language Processing | Generic |
| Intelligent Virtual Assistant | Natural Language Processing | Generic |
| Kaldi | Natural Language Processing | Specific |
| Latent Dirichlet Allocation | Natural Language Processing | Specific |
| Lexalytics | Natural Language Processing | Generic |
| Machine Translation | Natural Language Processing | Generic |
| Microsoft LUIS | Natural Language Processing | Specific |
| Natural Language Generation | Natural Language Processing | Specific |
| Natural Language Processing | Natural Language Processing | Specific |
| Natural Language Processing Systems | Natural Language Processing | Specific |
| Natural Language Programming | Natural Language Processing | Specific |
| Natural Language Toolkits | Natural Language Processing | Specific |
| Natural Language Understanding | Natural Language Processing | Specific |
| Natural Language User Interface | Natural Language Processing | Generic |
| Nearest Neighbour Algorithm | Natural Language Processing | Specific |
| OpenNLP | Natural Language Processing | Specific |
| Optical Character Recognition (OCR) | Natural Language Processing | Generic |
| Screen Reader | Natural Language Processing | Generic |
| Semantic Analysis | Natural Language Processing | Generic |
| Semantic Interpretation For Speech Recognition | Natural Language Processing | Generic |
| Semantic Parsing | Natural Language Processing | Generic |
| Semantic Search | Natural Language Processing | Generic |
| Sentiment Analysis | Natural Language Processing | Generic |
| Seq2Seq | Natural Language Processing | Specific |
| Speech Recognition | Natural Language Processing | Generic |
| Speech Recognition Software | Natural Language Processing | Generic |
| Statistical Language Acquisition | Natural Language Processing | Generic |
| Text Mining | Natural Language Processing | Specific |
| Tokenization | Natural Language Processing | Specific |
| Voice Interaction | Natural Language Processing | Generic |
| Voice User Interface | Natural Language Processing | Generic |
| Word Embedding | Natural Language Processing | Specific |
| Word2Vec Models | Natural Language Processing | Specific |
| Apache MXNet | Neural Networks | Specific |
| Artificial Neural Networks | Neural Networks | Specific |
| Autoencoders | Neural Networks | Specific |
| Caffe | Neural Networks | Specific |
| Caffe2 | Neural Networks | Specific |
| Chainer (Deep Learning Framework) | Neural Networks | Specific |
| Convolutional Neural Networks | Neural Networks | Specific |
| Cudnn | Neural Networks | Specific |
| Deep Learning | Neural Networks | Specific |
| Deeplearning4j | Neural Networks | Specific |
| Keras (Neural Network Library) | Neural Networks | Specific |
| Long Short‑Term Memory (LSTM) | Neural Networks | Specific |
| OpenVINO | Neural Networks | Specific |
| PaddlePaddle | Neural Networks | Specific |
| Pybrain | Neural Networks | Specific |
| Recurrent Neural Network (RNN) | Neural Networks | Specific |
| TensorFlow | Neural Networks | Specific |
| Advanced Robotics | Robotics | Specific |
| Cognitive Robotics | Robotics | Specific |
| Motion Planning | Robotics | Generic |
| Nvidia Jetson | Robotics | Specific |
| Robot Framework | Robotics | Specific |
| Robot Operating Systems | Robotics | Specific |
| Robotic Automation Software | Robotics | Specific |
| Robotic Liquid Handling Systems | Robotics | Specific |
| Robotic Programming | Robotics | Specific |
| Robotic Systems | Robotics | Specific |
| Servomotor | Robotics | Generic |
| SLAM Algorithms (Simultaneous Localization And Mapping) | Robotics | Generic |
| 3D Reconstruction | Visual Image Recognition | Generic |
| Activity Recognition | Visual Image Recognition | Generic |
| Computer Vision | Visual Image Recognition | Generic |
| Contextual Image Classification | Visual Image Recognition | Generic |
| Digital Image Processing | Visual Image Recognition | Generic |
| Eye Tracking | Visual Image Recognition | Generic |
| Face Detection | Visual Image Recognition | Generic |
| Facial Recognition | Visual Image Recognition | Generic |
| Image Analysis | Visual Image Recognition | Generic |
| Image Matching | Visual Image Recognition | Generic |
| Image Processing | Visual Image Recognition | Generic |
| Image Recognition | Visual Image Recognition | Generic |
| Image Segmentation | Visual Image Recognition | Generic |
| Image Sensor | Visual Image Recognition | Generic |
| Imagenet | Visual Image Recognition | Specific |
| Machine Vision | Visual Image Recognition | Generic |
| Motion Analysis | Visual Image Recognition | Generic |
| Object Recognition | Visual Image Recognition | Generic |
| OmniPage | Visual Image Recognition | Generic |
| Pose Estimation | Visual Image Recognition | Generic |
| Realsense | Visual Image Recognition | Specific |
Text descriptions
Text description of Figure 1
The chart titled "AI performance relative to the human baseline" tracks the progress of artificial intelligence across various tasks from 2012 to 2023. The vertical axis represents performance as a percentage relative to the human baseline, ranging from 0% to 120%. The horizontal axis represents the years from 2012 to 2023.
The chart includes the following tasks, each represented by a different colored line:
- **Image classification (orange)**: Starts around 85% in 2012 and gradually reaches 100% by 2015, maintaining that level through 2023.
- **Medium-level reading comprehension (green)**: Begins at 0% and shows significant growth starting around 2018, reaching 100% in 2023.
- **Basic-level reading comprehension (purple)**: Starts close to 80% in 2014, reaches 100% by 2018, and stays constant through 2023.
- **English language understanding (light blue)**: Begins around 2016 and climbs steadily, hitting 100% by 2020 and remaining stable.
- **Visual commonsense reasoning (brown)**: Starts near 0% around 2017, jumps to 80% by 2020, and reaches 100% by 2022.
- **Visual reasoning (green)**: Starts around 80% in 2016, gradually climbs to 100% by 2022.
- **Natural language inference (dark blue)**: Begins around 2016, grows to 100% by 2021.
- **Competition-level mathematics (cyan)**: Starts around 2018 at 0%, showing rapid growth to 100% by 2023.
- **Multitask language understanding (dark green)**: Starts at 0% in 2019 and reaches 100% by 2023.
Overall, the chart illustrates rapid advancements in AI capabilities, with most tasks achieving human-level performance or better by 2023.
Text description of Figure 2
The chart titled "Google search volume for 'artificial intelligence' in Australia (maximum=100)" shows the trend in search interest from 2017 to 2024. The vertical axis represents the search volume index, ranging from 0 to 100, with 100 being the maximum search interest. The horizontal axis represents the years from 2017 to 2024.
Key points of the chart:
- From 2017 to 2022, the search volume fluctuates between 30 and 60, with periodic peaks and troughs.
- There is a significant increase in search volume starting in late 2022, culminating in a peak at 100 in early 2023.
- This peak corresponds with the public release of ChatGPT, marked by an arrow labeled "ChatGPT publicly released."
- After the peak, the search volume drops but remains higher than previous years, fluctuating between 50 and 70 in 2023 and 2024.
Overall, the chart illustrates a significant spike in interest in artificial intelligence in Australia around the time of ChatGPT.
Text description of Figure 3
The chart titled "Figure 3: 'AI Jobs' as a proportion of SEEK job ads" shows the percentage of job advertisements on SEEK that are related to AI from 2017 to 2024. The y-axis represents the proportion of AI job ads, ranging from 0.00% to 0.25%. The x-axis represents the years from 2017 to 2024.
Key features of the chart:
- **2017**: The proportion of AI job ads starts below 0.05%.
- **2017 to 2019**: There is a steady increase in the proportion of AI job ads, reaching around 0.15% by the end of 2019.
- **2019 to 2020**: The growth continues but at a slower rate, peaking just below 0.20%.
- **2020 to 2022**: The proportion remains relatively stable, fluctuating around 0.20%.
- **2022 to 2024**: There is a gradual decline in the proportion of AI job ads, settling around 0.15% by 2024.
Overall, the chart illustrates a significant increase in the proportion of AI-related job ads from 2017 to 2019, followed by a period of stability and a slight decline in recent years.
Text description of Figure 4
The chart titled "Figure 4: Share of SEEK job ads including the phrase 'artificial intelligence'" shows the percentage of job advertisements on SEEK that mention "artificial intelligence" from 2017 to 2024. The y-axis represents the share of job ads, ranging from 0.00% to 0.18%. The x-axis represents the years from 2017 to 2024.
Key features of the chart:
- **2017**: The share of job ads mentioning "artificial intelligence" starts near 0.00%.
- **2017 to 2019**: There is a steady increase, reaching about 0.08% by the end of 2019.
- **2019 to 2020**: The share continues to rise, peaking around 0.14%.
- **2020 to early 2021**: The share fluctuates between 0.10% and 0.14%.
- **2021 to 2022**: There is a noticeable increase, peaking just above 0.16%.
- **2022 to 2024**: The share declines but remains higher than previous years, fluctuating between 0.10% and 0.12%.
Overall, the chart illustrates a growing trend in job ads mentioning "artificial intelligence" from 2017 to 2022, followed by a slight decline but maintaining a higher share than in the earlier years.
Text description of Figure 5
The chart titled "Figure 5: 'AI Jobs' as a share of all job ads, by occupation (top ten)" shows the proportion of job advertisements that mention AI across different occupations. The x-axis represents the share of job ads as a percentage, ranging from 0% to 7%. The y-axis lists the top ten occupations.
Key features of the chart:
- **Mathematical Science Professionals**: Approximately 6.5% of job ads in this category mention AI.
- **Life Scientists**: Around 6% of job ads include AI.
- **University Lecturers and Tutors**: About 4% of job ads mention AI.
- **Software and Applications Programmers**: Close to 3% of job ads mention AI.
- **Nurse Educators and Researchers**: Around 2.5% of job ads include AI.
- **Music Professionals**: Approximately 2% of job ads mention AI.
- **Social Professionals**: Close to 2% of job ads mention AI.
- **Electronics Engineers**: About 1.5% of job ads include AI.
- **Financial Brokers**: Around 1.5% of job ads mention AI.
- **Medical Scientists**: Close to 1.5% of job ads mention AI.
Overall, the chart indicates that occupations related to science, technology, engineering, and education have the highest share of job ads mentioning AI, with Mathematical Science Professionals and Life Scientists leading the list.
Text description of Figure 6
The chart titled "Figure 6: 'AI Jobs' as a share of all job ads, by SEEK classification" shows the proportion of job advertisements mentioning AI across various industry classifications on SEEK. The x-axis represents the share of job ads as a percentage, ranging from 0% to 3%. The y-axis lists the SEEK classifications.
Key features of the chart:
- **Science & Technology**: Nearly 3% of job ads in this category mention AI, the highest among all classifications.
- **Information & Communication Technology**: Close to 2.5% of job ads mention AI.
- **Banking & Financial Services**: Approximately 1.5% of job ads include AI.
- **Engineering**: Around 1.3% of job ads mention AI.
- **Education & Training**: About 1% of job ads mention AI.
- **Consulting & Strategy**: Close to 0.8% of job ads include AI.
- **Government & Defence**: Approximately 0.7% of job ads mention AI.
- **Insurance & Superannuation**: Close to 0.6% of job ads mention AI.
- **Advertising, Arts & Media**: Around 0.5% of job ads include AI.
- **Marketing & Communications**: Just below 0.5% of job ads mention AI.
- **Sales**: Close to 0.3% of job ads include AI.
- **Trades & Services**: Around 0.2% of job ads mention AI.
- **Sport & Recreation**: Approximately 0.2% of job ads include AI.
- **Call Centre & Customer Service**: Close to 0.2% of job ads mention AI.
- **CEO & General Management**: About 0.2% of job ads include AI.
- **Mining, Resources & Energy**: Just below 0.2% of job ads mention AI.
- **Farming, Animals & Conservation**: Around 0.15% of job ads include AI.
- **Healthcare & Medical**: Approximately 0.15% of job ads mention AI.
- **Manufacturing, Transport & Logistics**: Close to 0.15% of job ads include AI.
- **Human Resources & Recruitment**: About 0.1% of job ads mention AI.
- **Design & Architecture**: Just below 0.1% of job ads include AI.
- **Hospitality & Tourism**: Around 0.1% of job ads mention AI.
- **Administration & Office Support**: Close to 0.1% of job ads include AI.
- **Accounting**: Approximately 0.05% of job ads mention AI.
- **Legal**: Just below 0.05% of job ads include AI.
- **Construction**: Close to 0.05% of job ads mention AI.
- **Community Services & Development**: Around 0.03% of job ads include AI.
- **Real Estate & Property**: Just below 0.03% of job ads mention AI.
- **Retail & Consumer Products**: Around 0.02% of job ads include AI.
Overall, the chart illustrates that Science & Technology and Information & Communication Technology have the highest proportion of AI-related job ads, with other industries showing significantly lower shares.
* This analysis was carried out in collaboration with Matt Cowgill, formerly chief economist at SEEK Australia. My thanks to Matt for his careful datacrunching and SEEK for facilitating this research. No funding was provided for this research, and any errors are my responsibility alone.