Daron Acemoglu's insightful analysis in the Financial Times emphasizes the need for a measured perspective on AI's impact on productivity and economic growth. While his cautious approach is prudent, our experience at simplecrm.com offers a counterpoint:
1) Communication: Gen-AI tools have remarkably improved the quality and efficiency of our communications, streamlining internal and customer-facing interactions.
2) Developer Support: We have significantly enhanced the productivity and learning of our junior developers through automated code reviews/assistance, ensuring higher-quality outputs.
3) Knowledge Management: Implementing RAG-based knowledge bots has effectively leveraged organizational knowledge, proving indispensable in providing reliable information.
When implemented effectively, gen-AI can exceed the modest gains forecasted and transform productivity in practical settings.
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Daron Acemoglu is one of the top 3 most-cited living economists. [1] Did you see his research on generative AI?
His estimates are significantly more conservative than estimates of consultants that stand to profit from hype. He could be right:
- His methodology is quite clear; he classifies jobs and estimates how they would be impacted. [2]
- Productivity growth's relation with technology is complex. Personal computing revolution of the 70s and internet revolution of the 2000s did not accelerate productivity growth in the short to medium term. [3]
- He doesn't have much to gain or lose from the hype.
Before buying the next pricy enterprise generative AI platform or project, enterprises should consider whether it will make a big enough difference.
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[1] Wikipedia https://lnkd.in/gUBWdxnR
[2] The Simple Macroeconomics of AI https://lnkd.in/ggpZrX6A
[3] Productivity paradox https://lnkd.in/gpe_8Cdq
Daron Acemoglu is one of the top 3 most-cited living economists. [1] Did you see his research on generative AI?
His estimates are significantly more conservative than estimates of consultants that stand to profit from hype. He could be right:
- His methodology is quite clear; he classifies jobs and estimates how they would be impacted. [2]
- Productivity growth's relation with technology is complex. Personal computing revolution of the 70s and internet revolution of the 2000s did not accelerate productivity growth in the short to medium term. [3]
- He doesn't have much to gain or lose from the hype.
Before buying the next pricy enterprise generative AI platform or project, enterprises should consider whether it will make a big enough difference.
***
Follow me for latest in B2B tech
Ring the 🔔 on my profile for notifications
***
[1] Wikipedia https://lnkd.in/gUBWdxnR
[2] The Simple Macroeconomics of AI https://lnkd.in/ggpZrX6A
[3] Productivity paradox https://lnkd.in/gpe_8Cdq
Technology Consultant: Author of Unicorns, Hype and Bubbles
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of #AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the #productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising #technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
#technology#innovation#artificialintelligencehttps://lnkd.in/gv5yb3Mr
MIT professor predicts that AI will have a modest impact on productivity, contributing to a growth of just 0.66% over 10 years (0.06% annually). Unlike past technologies, such as industrial robots, AI struggles with most manual and social tasks. Its effectiveness is further limited by complex tasks like evaluating applications and diagnosing health problems.
#AI#Technology
Technology Consultant: Author of Unicorns, Hype and Bubbles
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of #AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the #productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising #technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
#technology#innovation#artificialintelligencehttps://lnkd.in/gv5yb3Mr
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of #AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the #productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising #technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
#technology#innovation#artificialintelligencehttps://lnkd.in/gv5yb3Mr
Enterprising Chief Officer & Product Evangelist | MDDI's Voice to Follow in 2024 | IP Portfolio Leadership l Excellence In Innovation & Commercialization l M&A/IPO/Startups l Board & Investor Advisor Delivering ROI
If this analysis is focused entirely on LLMs, I can see that. Meanwhile, the productivity of AI in other applications is enormous. For example, it has been shown through extensive research that an AI can detect the embedded nerves in the mastoid and build virtual fixtures around them. This has the potential of increasing the speed of a mastoidectomy from about 90 minutes to under 20. Project this savings of OR time over all middle ear surgeries, and you have an avalanche of benefits from one single procedural improvement.
Or automated drone inspections of windmill turbines...the cost improvements and safety enhancements are phenominal.
Of course, with all things, your mileage may vary. If you choose to look for where AI isn't useful today, you risk missing where the benefits have already caused massive shifts for businesses.
Technology Consultant: Author of Unicorns, Hype and Bubbles
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of #AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the #productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising #technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
#technology#innovation#artificialintelligencehttps://lnkd.in/gv5yb3Mr
McKinsey’s latest report predicts that AI could generate up to $23 trillion in economic value annually by 2040.
In Episode 122 of The Artificial Intelligence Show, Paul Roetzer breaks down what this could mean for industries worldwide.
👉 Read more at the link below
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of hashtag
#AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the hashtag
#productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising hashtag
#technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
hashtag
#technology hashtag
#innovation hashtag
#artificialintelligence
Technology Consultant: Author of Unicorns, Hype and Bubbles
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of #AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the #productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising #technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
#technology#innovation#artificialintelligencehttps://lnkd.in/gv5yb3Mr
#Data and #reason will win the day. #AI will be a powerful tool in a few areas of tedious human endeavor. It's not going to have the impact that the hypesters and doomers fancy. Does this mean it's a waste? No.
AI will be a fundamental shift in how we approach certain problems. It will shift some jobs from one thing to another, just as multiple technological achievements have in the past.
#Innovators will find practical uses not yet even imagined. Problems will be solved in creative and surprising ways. All we need to do now is to set aside our irrational fears and our wild-eyed fantasies of creating artificial general (i.e. human) #intelligence as some kind of apocalyptic replacement for humanity.
Technology Consultant: Author of Unicorns, Hype and Bubbles
MIT professor of economics says AI won’t improve productivity growth anywhere near what the optimists say. Daron Acemoglu estimates total factor productivity growth will increase “by only 0.66% over 10 years, or by 0.06% annually.”
How does he do this calculation? He begins with cost savings estimated by other economists for specific tasks. Two economists “examined the impact of ChatGPT on simple writing tasks (such as summarizing documents or writing routine grant proposals or marketing material), while” others “assessed the use of #AI assistants in customer service. Taken together, this research suggests that currently available generative-AI tools yield average labor-cost savings of 27% and overall cost savings of 14.4%.” Acemoglu uses these estimates to calculate the impact on the total economy by knowing the extent of these tasks in the economy.
“These figures are much smaller than the ones from Goldman Sachs and McKinsey. If you want to get those bigger numbers, you either must boost the #productivity gains at the micro level or assume that many more tasks in the economy will be affected.”
Acemoglu claims the optimistic estimates “fall out of the range offered by existing studies; they also do not align with the observed effects of other, even more promising #technologies. For example, industrial robots have transformed some manufacturing sectors, and they appear to have reduced labor costs by about 30%.”
“Similarly, we are unlikely to see far more than 4.6% of tasks being taken over, because AI is nowhere close to being able to perform most manual or social tasks (including seemingly simple functions with some social aspects, like accounting).”
Acemoglu agrees that “AI could have larger effects than my analysis allows if it revolutionizes the process of scientific discovery or creates many new tasks and products.” “But these breakthroughs are unlikely to be a major source of economic growth within 10 years.”
He also argues his “own estimates could be too high. Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. Here, the model can learn on the basis of outside information and readily available historical data.”
However, “many of the 4.6% of tasks that could feasibly be automated within 10 years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). In these cases, learning from outside observation is much harder, and generative AI models must rely instead on the behavior of existing workers.”
#technology#innovation#artificialintelligencehttps://lnkd.in/gv5yb3Mr