[Mick]

There is another good example in history, the development of the ATM.  It turns out that while many people predicted that the widespread deployment of ATMs would lead to fewer jobs for bank tellers that they were replacing, paradoxically employment of bank tellers actually increased, as shown below.



Why?  Well, while the number of tellers per branch went down substantially, the number of bank branches expanded dramatically, because now it was cheaper to run a branch than it was prior to ATMs.  The nature of the job changed a bit, and tellers were relieved of routine stuff such as depositing checks and withdrawing cash (which was now done by ATMs), and were able to be used to build relationships and open accounts, activities that were much more valuable to the banks.

What does it mean for a company like [my company]? Let’s imagine that due to AI all of us can all of a sudden be 20% more efficient in what we do.  I suppose one thing that [the company] could do is lay 20% of the company off and return the money to shareholders.  But there is not a fixed pot of drugs to be developed, and an alternate use of the 20% of excess productivity is to work on 20% more programs.  With a 20% improvement in efficiency, suddenly some programs that may not have been viable to pursue because aNPV was marginal, might all of a sudden be viable because it requires 20% fewer resources to pursue.  Maybe we pursue an additional indication for a drug already in our portfolio in parallel instead of gating it.  The point is that if we were 20% more efficient, it might not mean that 20% of us are let go, but rather that we have a chance to work on 20% more interesting programs.  In fact, as with the bank teller example, if we are 20% more efficient overall, the cost/benefit balance of adding employees becomes favorable, since their extra productivity may make taking on the extra FTE to be worth it.  Now, of course, it is never that simple, and AI will have variable effects on productivity, meaning that the effect will be unevenly distributed, which means there will be different productivity bottlenecks over time, so the relative job composition in the company will probably change.  I hope that impact is minimal, and I certainly hope that my job is made more productive rather than redundant by AI, but it is a fine line between the two.

Apologies for the rambling response; I’m curious what other people think.  Do I have my head in the sand about AI?  Am I too pessimistic about the pace of deployment?


BC

20+ years ago, I was at a Big Five auditing firm and several very interesting technology-related incidents occurred that remind me of your ATM example:

1) There was a strategy paper rolled out internally that indicated that due to technology advances, the firm would only need half as many auditors as it currently employed within five years. There was much sturm und drang internally. By somewhat happy/unhappy happenstance, Enron became the last in a long line of Arthur Andersen attest failures (Waste Management, MCI, etc.), and when Enron went under, it (a) dragged Andersen down with them and (b) caused Congress to implement the Sarbanes-Oxley act. Between the Andersen clients fleeing a failing AA for another auditor and Sec. 404 of the S-O act, which effectively doubled the amount of attest work that needed to be done for publicly traded entities, they were able to keep all their auditors and hire more.

Incidentally, Andersen Tax managed to reconstitute itself, and they just went public yesterday, ringing the bell at the NYSE (ANDG). When I was a young marketer, I created the single most outlandishly successful proposal our tech team ever did with the help and active participation of their current chairman, who was a young tax partner then.

2) Marketing took it in the shorts. My team and I were working 100 hour weeks for several months to accommodate all the new business. We were congratulating ourselves as to how the firm would reward us for our intrepidity. Imagine my surprise when I have to speak to the chairman with my two peers. We're waiting in his conference room, and if you put three senior marketers together, we can't help but use flip charts and jot down ideas (2o years ago). There was one flip chart in the room, and as we flipped through it to find a blank page, we flipped over a sheet that contained calculations on how much money the firm's partners would save/make if they eliminated the entire marketing department. Because at that point, they had all the business they could digest and wouldn't need marketing for five years, as they helpfully explained to me. Hence, my mid-career jaunt to the legal profession.

3) I use four AI tools pretty regularly. I just gave them a fairly straightforward request having to do with summarizing HR policies. They came up with four similar, yet very different answers.

4) I created an index the component parts of which added up to 100. One of the AI tools routinely added the components up to 102. Meh 

5) Perhaps this is overly simplistic, but my biggest issue with AI is its right-brained thinking. Yes, it creates and collates work in 5% of the time that a mid-level career person can do...but AI can't ideate worth a damn, not yet anyway, not at the level that I would find interesting or compelling.

[Mick]

There is another good example in history, the development of the ATM.  It turns out that while many people predicted that the widespread deployment of ATMs would lead to fewer jobs for bank tellers that they were replacing, paradoxically employment of bank tellers actually increased, as shown below.



Why?  Well, while the number of tellers per branch went down substantially, the number of bank branches expanded dramatically, because now it was cheaper to run a branch than it was prior to ATMs.  The nature of the job changed a bit, and tellers were relieved of routine stuff such as depositing checks and withdrawing cash (which was now done by ATMs), and were able to be used to build relationships and open accounts, activities that were much more valuable to the banks.

What does it mean for a company like [my company]? Let’s imagine that due to AI all of us can all of a sudden be 20% more efficient in what we do.  I suppose one thing that [the company] could do is lay 20% of the company off and return the money to shareholders.  But there is not a fixed pot of drugs to be developed, and an alternate use of the 20% of excess productivity is to work on 20% more programs.  With a 20% improvement in efficiency, suddenly some programs that may not have been viable to pursue because aNPV was marginal, might all of a sudden be viable because it requires 20% fewer resources to pursue.  Maybe we pursue an additional indication for a drug already in our portfolio in parallel instead of gating it.  The point is that if we were 20% more efficient, it might not mean that 20% of us are let go, but rather that we have a chance to work on 20% more interesting programs.  In fact, as with the bank teller example, if we are 20% more efficient overall, the cost/benefit balance of adding employees becomes favorable, since their extra productivity may make taking on the extra FTE to be worth it.  Now, of course, it is never that simple, and AI will have variable effects on productivity, meaning that the effect will be unevenly distributed, which means there will be different productivity bottlenecks over time, so the relative job composition in the company will probably change.  I hope that impact is minimal, and I certainly hope that my job is made more productive rather than redundant by AI, but it is a fine line between the two.

Apologies for the rambling response; I’m curious what other people think.  Do I have my head in the sand about AI?  Am I too pessimistic about the pace of deployment?


BC

20+ years ago, I was at a Big Five auditing firm and several very interesting technology-related incidents occurred that remind me of your ATM example:

1) There was a strategy paper rolled out internally that indicated that due to technology advances, the firm would only need half as many auditors as it currently employed within five years. There was much sturm und drang internally. By somewhat happy/unhappy happenstance, Enron became the last in a long line of Arthur Andersen attest failures (Waste Management, MCI, etc.), and when Enron went under, it (a) dragged Andersen down with them and (b) caused Congress to implement the Sarbanes-Oxley act. Between the Andersen clients fleeing a failing AA for another auditor and Sec. 404 of the S-O act, which effectively doubled the amount of attest work that needed to be done for publicly traded entities, they were able to keep all their auditors and hire more.

Incidentally, Andersen Tax managed to reconstitute itself, and they just went public yesterday, ringing the bell at the NYSE (ANDG). When I was a young marketer, I created the single most outlandishly successful proposal our tech team ever did with the help and active participation of their current chairman, who was a young tax partner then.

2) Marketing took it in the shorts. My team and I were working 100 hour weeks for several months to accommodate all the new business. We were congratulating ourselves as to how the firm would reward us for our intrepidity. Imagine my surprise when I have to speak to the chairman with my two peers. We're waiting in his conference room, and if you put three senior marketers together, we can't help but use flip charts and jot down ideas (2o years ago). There was one flip chart in the room, and as we flipped through it to find a blank page, we flipped over a sheet that contained calculations on how much money the firm's partners would save/make if they eliminated the entire marketing department. Because at that point, they had all the business they could digest and wouldn't need marketing for five years, as they helpfully explained to me. Hence, my mid-career jaunt to the legal profession.

3) I use four AI tools pretty regularly. I just gave them a fairly straightforward request having to do with summarizing HR policies. They came up with four similar, yet very different answers.

4) I created an index the component parts of which added up to 100. One of the AI tools routinely added the components up to 102. Meh 

5) Perhaps this is overly simplistic, but my biggest issue with AI is its right-brained thinking. Yes, it creates and collates work in 5% of the time that a mid-level career person can do...but AI can't ideate worth a damn, not yet anyway, not at the level that I would find interesting or compelling.