Sign up to access an inspiration library of 30,000+ posts
John Bailey's Recent LinkedIn Posts
John Bailey
@johnbaileyfi
Experienced Data & AI strategist and communicator | Cross-industry | Cross-organization
Posts
John Bailey
Tech & AI
3mo
I’ve recently added a concept on automation by Erik Brynjolfsson from 2022 (pictured below) to some of my presentations on the future of work at the dawn of AI, and I’m curious whether this resonates with you as well.
I like this model because it’s one of the clearest ways I’ve found to move the discussion away from extremes. Too often AI is framed either as an economic saviour or as a workforce destroyer. In practice, the impact of the latest wave of AI is (at least for now) much more granular.
Brynjolfsson’s model is task-based. Instead of asking whether “jobs” disappear, it asks what happens to tasks:
- some tasks remain best done by humans
- some tasks become automatable by machines
- and a third category expands: new tasks humans can do with machines
While most of the discussion tends to fixate on the first two areas (both the optimistic and pessimistic takes), it’s actually the third area I find most important right now: the growing “human + machine” task space.
Marc Andreessen recently expressed a similar idea in a more direct way: it’s less about job loss and more about task loss. Roles tend to persist, but the task lists inside them change, sometimes dramatically. He also noted an interesting side effect in product teams: with today’s AI tools, product owners, designers, and developers can each feel like they can cover parts of the other two roles. Not because the roles no longer matter, but because the boundaries between tasks start to soften.
If that still sounds abstract, software development is an easy example. For years, “software development” was treated as a synonym for “coding,” largely because coding was the bottleneck. With AI tools, producing first drafts of code is becoming cheaper and faster. But the work doesn’t disappear. The centre of gravity shifts toward problem framing, setting constraints, evaluating outputs, integration, and accountability.
From a leadership perspective, this is why I think the “job loss vs. salvation” debate is the wrong starting point. The strategic questions are more practical:
- Where in our organisations are tasks moving into the human+machine space right now?
- Where do we find the new bottlenecks, what becomes scarce?
- What have we previously left undone because of ROI, that might now become feasible?
Curious how others are seeing this: where do you notice the task mix changing first in your own work?
120
John Bailey
Tech & AI
3mo
I’ve recently added a concept on automation by Erik Brynjolfsson from 2022 (pictured below) to some of my presentations on the future of work at the dawn of AI, and I’m curious whether this resonates with you as well.
I like this model because it’s one of the clearest ways I’ve found to move the discussion away from extremes. Too often AI is framed either as an economic saviour or as a workforce destroyer. In practice, the impact of the latest wave of AI is (at least for now) much more granular.
Brynjolfsson’s model is task-based. Instead of asking whether “jobs” disappear, it asks what happens to tasks:
- some tasks remain best done by humans
- some tasks become automatable by machines
- and a third category expands: new tasks humans can do with machines
While most of the discussion tends to fixate on the first two areas (both the optimistic and pessimistic takes), it’s actually the third area I find most important right now: the growing “human + machine” task space.
Marc Andreessen recently expressed a similar idea in a more direct way: it’s less about job loss and more about task loss. Roles tend to persist, but the task lists inside them change, sometimes dramatically. He also noted an interesting side effect in product teams: with today’s AI tools, product owners, designers, and developers can each feel like they can cover parts of the other two roles. Not because the roles no longer matter, but because the boundaries between tasks start to soften.
If that still sounds abstract, software development is an easy example. For years, “software development” was treated as a synonym for “coding,” largely because coding was the bottleneck. With AI tools, producing first drafts of code is becoming cheaper and faster. But the work doesn’t disappear. The centre of gravity shifts toward problem framing, setting constraints, evaluating outputs, integration, and accountability.
From a leadership perspective, this is why I think the “job loss vs. salvation” debate is the wrong starting point. The strategic questions are more practical:
- Where in our organisations are tasks moving into the human+machine space right now?
- Where do we find the new bottlenecks, what becomes scarce?
- What have we previously left undone because of ROI, that might now become feasible?
Curious how others are seeing this: where do you notice the task mix changing first in your own work?
120
John Bailey
Tech & AI
3mo
I’ve recently added a concept on automation by Erik Brynjolfsson from 2022 (pictured below) to some of my presentations on the future of work at the dawn of AI, and I’m curious whether this resonates with you as well.
I like this model because it’s one of the clearest ways I’ve found to move the discussion away from extremes. Too often AI is framed either as an economic saviour or as a workforce destroyer. In practice, the impact of the latest wave of AI is (at least for now) much more granular.
Brynjolfsson’s model is task-based. Instead of asking whether “jobs” disappear, it asks what happens to tasks:
- some tasks remain best done by humans
- some tasks become automatable by machines
- and a third category expands: new tasks humans can do with machines
While most of the discussion tends to fixate on the first two areas (both the optimistic and pessimistic takes), it’s actually the third area I find most important right now: the growing “human + machine” task space.
Marc Andreessen recently expressed a similar idea in a more direct way: it’s less about job loss and more about task loss. Roles tend to persist, but the task lists inside them change, sometimes dramatically. He also noted an interesting side effect in product teams: with today’s AI tools, product owners, designers, and developers can each feel like they can cover parts of the other two roles. Not because the roles no longer matter, but because the boundaries between tasks start to soften.
If that still sounds abstract, software development is an easy example. For years, “software development” was treated as a synonym for “coding,” largely because coding was the bottleneck. With AI tools, producing first drafts of code is becoming cheaper and faster. But the work doesn’t disappear. The centre of gravity shifts toward problem framing, setting constraints, evaluating outputs, integration, and accountability.
From a leadership perspective, this is why I think the “job loss vs. salvation” debate is the wrong starting point. The strategic questions are more practical:
- Where in our organisations are tasks moving into the human+machine space right now?
- Where do we find the new bottlenecks, what becomes scarce?
- What have we previously left undone because of ROI, that might now become feasible?
Curious how others are seeing this: where do you notice the task mix changing first in your own work?
120