Slow-burn AI: When augmentation, not automation, is the real threat

A common meme on the impact of Gen AI on jobs goes something like this:

“AI won’t take your job but someone using AI might.”

You’d be quick to conclude then, that, those who start using AI will win and those who don’t use AI will lose.

But what if those who use AI also lose?

Well… Not all of them, but enough of them to make you sit up and notice.

Automation bad, augmentation good?

The popular narrative around the impact of AI on jobs goes something like this:

1. AI can lead to automation or augmentation.
2. Automation eats your job.
3. Augmentation makes you better at your job.
4. Hence, avoid automation and embrace augmentation.

This argument is as flawed as it is simplistic.

The argument possibly worked well in a pre-internet world where robotic automation repeatedly substituted factory jobs.

However, with the rise of online platforms, and the networked markets of labor that emerge as a result, automation is only a short term threat to jobs.

A far greater threat now emerges: the long-term commoditization of jobs through augmentation.

Drawing on my extensive work with the ILO’s Future of Work Commission, I lay out the rationale for AI-driven commoditization of work and how that gets accelerated in a world of digital platforms.

Unbundling and rebundling of work

Every job is a bundle of tasks.

Some of these tasks require specialization, some don’t, but they still remain part of the bundle as the cost of unbundling and delegating those tasks may be high.

Every new technology wave (including the ongoing rise of Gen AI) attacks this bundle.

New technology may substitute a specific task – for instance, intelligent scheduling tools may fully substitute a task previously performed by a human.

New technology may also complement a specific task – for instance, improvements in AI may provide diagnostic support to doctors and radiologists, enhancing their ability to perform the task.

Automation is technology in substitute mode.

Augmentation is a result of technology working as a complement.

First order thinking suggests that technology-as-substitute (automation) is bad, and that technology-as-complement (augmentation) is good.

However, this reasoning ignores the effects of feedback loops and their compounding effects over time.

But, let’s start at the very beginning…

The chainsaw massacre of jobs

Up until the start of the twentieth century, axe-wielding was a high-skilled job. In order to be any good at logging, you needed to perfect the right angle of the axe swing as well as the grip on the axe, through years of practice, that developed both muscle and muscle memory.

The invention of the chainsaw changed all of that. Low-skilled loggers, who lacked the knowledge or the muscle memory to perform well, could now perform at a much higher level of effectiveness.

One way to think about this is that this chainsaw augmentation helped loggers level-up and get better at their job.

But that’s only half the story. The chainsaw helped low-skill loggers level-up but didn’t benefit high-skilled loggers quite as much.

The real long term effect of the chainsaw was the commoditization of logging as a job.

Since anyone could now be a logger, the job’s ability to command a skill-premium eroded. As the market of potential loggers exploded, wages stabilised and the high-skilled loggers lost the ability to charge a premium. Yes, increase in logging also drove increased demand downstream in the short term but those effects soon stabilized and logging became a commoditized job.

When technology augments skilled work and enables historically low-skilled workers to perform at par with historically high-skilled workers, such augmentation makes workers more substitutable and eventually commoditizes the job.

Generative chainsaw massacre

But, surely, this doesn’t apply to knowledge work… or does it?

A recent much-cited HBS study on the effects of Generative AI augmentation on BCG consultants tested performance across a sample of BCG consultants before and after AI augmentation (impact of AI augmentation on performance).

The study reveals two interesting findings:

1. Consultants who tested in the lower half of the group when not using AI increased the quality of their outputs by 43% went using AI. Consultants who tested in the top half of the group when not using AI increased the quality of their outputs by only 17% with AI.
2. 1. Without AI, the score gap between the top and bottom performers was 20%+ (4.5 vs 5.2). With AI augmentation, the score gap had collapsed to 4%.

   This is an early study, possibly with many caveats, but it demonstrates one important point.

   AI augmentation makes high-skilled knowledge workers relatively more substitutable by lowering the skill barrier to achieve the same performance.

   Augmentation improves productivity and output but does so to a greater extent for those with lower skills than for those with higher skills. When this plays out, the worker becomes more substitutable as the skill becomes more commoditized.

   Similar studies have been carried out with customer service agents, law students, and writers. All these studies demonstrate similar results. What happened with the chainsaw is increasingly possible across a lot of knowledge work in the age of Gen AI.

   AI + platforms = Accelerated substitutability

   AI augmentation commoditizes knowledge workers making them more substitutable.

   Skill commoditization is the first piece in the puzzle here.

   But these effects are particularly amplified in a world of online platforms. Platforms create markets over which skills can be traded.

   Centralized market-making accelerates worker substitutability when augmented workers have largely undifferentiated skills.

   The Uber for X is back… and it wants your job

   Cabbies and taxi drivers have traditionally had a significant knowledge advantage in their work – knowledge of the city’s maps and navigation, a mental model they constantly update with the latest information as they go about their job.

   This was a significant knowledge-based barrier to entry.

   That is, until Google Maps came along and commodified this knowledge making it readily accessible to anyone. And until Uber came along and created a ‘wrapper’ over Google Maps, making this knowledge component freely accessible to anyone.

   There were four effects that played out:

   1. Commoditization of skill

   First, London cabbies lost their competitive advantage. Any maps-augmented amateur could compete with their city navigation skills even if they couldn’t immediately compete with their driving skills.

   2. Supply expansion erodes skill premium

   Second, as ride-hailing apps integrated maps with the ability to charge for a ride, the market of potential drivers expanded, leading to greater competition and an overall ‘price war’, which eroded the ability of high-skilled drivers to charge a premium.

   3. Centralized market-making drives faster commoditization

   Third, and most important, ride-hailing marketplaces like Uber centralized this growing market and absorbed this ‘price war’ into their algorithm, effectively driving down (and standardizing) the cost of the ride and the payout to the driver. Hailing a cab off the road – the last remaining information advantage where a cabbie could charge a premium on account of being at the right place at the right time – was lost.

   4. Centralized ‘job discovery’ reduces negotiating power

   Finally, the advent of Uber changed the mechanics of ‘job discovery’, or in this case the mechanics of finding your next ride. Cabbies could either be assigned jobs by the algorithm and compete with all other 5-star-rated, maps-augmented amateur drivers or they could stay out of the system and miss out on the demand coming in through Uber. Cabbies lost their negotiation power leading to a variety of effects – they had to accept rides without knowing the destination, they had to adhere to acceptance and cancellation rate metrics. They had lost not only the ability to set the price but also the agency to accept or reject work opportunities based on whether it made commercial sense.

   These four effects, together, drove commoditization of the cabbie’s ‘job’, effectively leading to value migration away from the drivers to market-makers like Uber.

   Uber, for its own part, hasn’t been short on efforts to move the market from commoditization to substitution, investing heavily in self-driving cars. While there are multiple factors holding back a future of self-driving cars, there is no denying the role that today’s drivers play in potentially training self-driving technologies every time they set out on a maps-augmented drive.

   Skill absorption compounds substitutability

   With skill commoditization and centralized market-making out of the way, we now look at the third key factor – skill absorption.

   Chainsaws made loggers more substitutable and logging more commoditized.

   But this was largely a one-time effect. The wage market for logging largely stabilized after the effect had played out.

   Things play out a little differently with AI.

   AI compounds commoditization by continuous absorption of skills.

   The more successful the AI is at augmentation, the more effective it gets at future augmentation.