Whenever a technology has increased productivity, the extra profit made hasn’t been passed on to increase the workers’ real wages. Why would it?
We’ve already seen AI preemptively treated as a way to make workers redundant and not pay their wage at all, with some idiot bosses having to rehire entire teams they had fired because they bought into AI hype and thought it was capable of replacing them all. They’ve shown what their financial incentive is - increasing shareholder value by outsourcing to cheap markets or removing jobs. And in fact, removing jobs through automation could be a great thing if we had a market capable of retraining those workers to perform the jobs that society needs most. We don’t. Our political-economy is run for profit, not productivity, and it’s important we recognise how contradictory those goals truly are in the real world.
Whenever a technology has increased productivity, the extra profit made hasn’t been passed on to increase the workers’ real wages. Why would it?
Where did you read this? It certainly did in the past:
It stopped when a combination of the mining boom taking off, Australia being too expensive to be a manufacturing hub anymore and it’s difficult to measure how many widgets we make when the widgets are hard to measure according to:
spoiler
This article by Ian Verrender examines the concept of productivity in Australia and challenges the common narrative that our productivity is in crisis.
Key points:
Misunderstanding Productivity: Many people, especially in business, confuse productivity (output per unit of input) with profitability and offer simplistic solutions like cutting red tape and lowering taxes without fully understanding the issue or measuring it accurately.
Wage Growth’s Role: Contrary to common belief, stagnant wages growth isn’t necessarily bad for productivity. Higher wages incentivize efficiency improvements and labor-saving technology (as seen in Australia’s historical shift from agriculture to manufacturing/services), which wouldn’t happen with suppressed wages.
Productivity Measurement Challenges: Measuring productivity is difficult, particularly in service-based economies like Australia’s. It’s hard to quantify output accurately for sectors like education or healthcare. This makes interpreting statistics and identifying true trends complex.
The Mining Boom Effect: Periods of high mineral prices (like the GFC aftermath and pandemic years) can appear to lower productivity figures, not because the economy is less efficient, but because mining companies become profitable digging harder-to-reach resources, taking more time and labor per unit. This isn’t necessarily a sign of economic decline.
Investment, Not Just Taxes: While tax cuts might increase profits, they don’t automatically lead to investment in productivity-enhancing equipment or techniques unless coupled with wage growth that makes such investment viable for businesses. Encouraging investment through targeted incentives could be more effective.
Complexity and Interconnectedness: Economic issues are complex. Solutions often have unintended consequences. The author questions the practicality of drastically changing policies (like abandoning mining) to achieve potentially easier-to-measure productivity figures, suggesting a deeper analysis is needed instead of assuming a simple fix exists.
In essence, Verrender argues that Australia shouldn’t be in a panic about productivity, that wage growth isn’t inherently bad, measurement methods are flawed for our service economy, and the mining boom’s impact is often misinterpreted. He suggests focusing on encouraging genuine investment rather than relying solely on tax cuts or wage restraint.
removing jobs through automation could be a great thing if we had a market capable of retraining those workers to perform the jobs that society needs most
I’m not convinced that the ABS graph shows that productivity and earnings were closely coupled before or during the 90s. As it says in the graph title, they’ve set 1991 as a starting origin (setting Productivity equal to Earnings), so it doesn’t imply the two were already as closely coupled as they look. They only appear so close because the graph sets 1991 as the common point to compare both axes.
To demonstrate, I’ve edited the graph to show what would happen if they made that same graph start from 2009. I’ve done this by copying the orange line up (and colouring it red) so that both lines begin at the same spot in 2009 instead of 1991. And just like the 1991 line, they appear to match each other for a few years - apart from one major dip around 2016, they align very closely for the first 10 years just like in the full 1991 graph.
But we know from your original ABS graph that the wages were already significantly diverging from productivity by 2009. So, I suspect that if we had a longer graph, then we’d learn that wages were already decoupled from productivity in the decades before 1991, but at the very least this graph doesn’t imply close coupling existed in the past and shows evidence of regular uncoupling.
Maybe we should use AI to train them :D
Sure. Although like all tools, AI can only help if used properly. It’s not a panacea, and it can’t replace most training techniques by itself. Similarly, we can’t just “use the internet” to train them or “use books” to train them.
Whenever a technology has increased productivity, the extra profit made hasn’t been passed on to increase the workers’ real wages. Why would it?
We’ve already seen AI preemptively treated as a way to make workers redundant and not pay their wage at all, with some idiot bosses having to rehire entire teams they had fired because they bought into AI hype and thought it was capable of replacing them all. They’ve shown what their financial incentive is - increasing shareholder value by outsourcing to cheap markets or removing jobs. And in fact, removing jobs through automation could be a great thing if we had a market capable of retraining those workers to perform the jobs that society needs most. We don’t. Our political-economy is run for profit, not productivity, and it’s important we recognise how contradictory those goals truly are in the real world.
Where did you read this? It certainly did in the past:
It stopped when a combination of the mining boom taking off, Australia being too expensive to be a manufacturing hub anymore and it’s difficult to measure how many widgets we make when the widgets are hard to measure according to:
spoiler
This article by Ian Verrender examines the concept of productivity in Australia and challenges the common narrative that our productivity is in crisis.
Key points:
Misunderstanding Productivity: Many people, especially in business, confuse productivity (output per unit of input) with profitability and offer simplistic solutions like cutting red tape and lowering taxes without fully understanding the issue or measuring it accurately.
Wage Growth’s Role: Contrary to common belief, stagnant wages growth isn’t necessarily bad for productivity. Higher wages incentivize efficiency improvements and labor-saving technology (as seen in Australia’s historical shift from agriculture to manufacturing/services), which wouldn’t happen with suppressed wages.
Productivity Measurement Challenges: Measuring productivity is difficult, particularly in service-based economies like Australia’s. It’s hard to quantify output accurately for sectors like education or healthcare. This makes interpreting statistics and identifying true trends complex.
The Mining Boom Effect: Periods of high mineral prices (like the GFC aftermath and pandemic years) can appear to lower productivity figures, not because the economy is less efficient, but because mining companies become profitable digging harder-to-reach resources, taking more time and labor per unit. This isn’t necessarily a sign of economic decline.
Investment, Not Just Taxes: While tax cuts might increase profits, they don’t automatically lead to investment in productivity-enhancing equipment or techniques unless coupled with wage growth that makes such investment viable for businesses. Encouraging investment through targeted incentives could be more effective.
Complexity and Interconnectedness: Economic issues are complex. Solutions often have unintended consequences. The author questions the practicality of drastically changing policies (like abandoning mining) to achieve potentially easier-to-measure productivity figures, suggesting a deeper analysis is needed instead of assuming a simple fix exists.
In essence, Verrender argues that Australia shouldn’t be in a panic about productivity, that wage growth isn’t inherently bad, measurement methods are flawed for our service economy, and the mining boom’s impact is often misinterpreted. He suggests focusing on encouraging genuine investment rather than relying solely on tax cuts or wage restraint.
https://www.abc.net.au/news/2025-05-27/productivity-wages-growth-australia-mining-boom/105338488
Maybe we should use AI to train them :D
I’m not convinced that the ABS graph shows that productivity and earnings were closely coupled before or during the 90s. As it says in the graph title, they’ve set 1991 as a starting origin (setting Productivity equal to Earnings), so it doesn’t imply the two were already as closely coupled as they look. They only appear so close because the graph sets 1991 as the common point to compare both axes.
To demonstrate, I’ve edited the graph to show what would happen if they made that same graph start from 2009. I’ve done this by copying the orange line up (and colouring it red) so that both lines begin at the same spot in 2009 instead of 1991. And just like the 1991 line, they appear to match each other for a few years - apart from one major dip around 2016, they align very closely for the first 10 years just like in the full 1991 graph.
But we know from your original ABS graph that the wages were already significantly diverging from productivity by 2009. So, I suspect that if we had a longer graph, then we’d learn that wages were already decoupled from productivity in the decades before 1991, but at the very least this graph doesn’t imply close coupling existed in the past and shows evidence of regular uncoupling.
Sure. Although like all tools, AI can only help if used properly. It’s not a panacea, and it can’t replace most training techniques by itself. Similarly, we can’t just “use the internet” to train them or “use books” to train them.