But much of the disparity in views about AI is in the timing of benefits, not ultimate value.
The key phrase might be “in the short term.” Among the “pessimists” cited is Daron Acemoglu, MIT Institute professor.
Acemoglu forecasts about a 0.5 percent increase in productivity and about a one percent increase in gross domestic product in the next 10 years, compared with Goldman Sachs estimates of a nine percent increase in productivity and 6.1 percent increase in GDP.
“The forecast differences seem to revolve more around the timing of AI’s economic impacts than the ultimate promise of the technology,” he argues.
And much could hinge on “how” generative AI develops and what it replaces. For example, if GenAI winds up replacing low-wage jobs with costly technology, without producing other value, then the investments might be wasted.
One might argue that the opposite has been the case for some successful technology transitions of the past, including the internet, where relatively low-cost technology replaced costly incumbent solutions.
Seen in that light, a potential problem with generative AI is that it is a costly investment that almost has to displace complex problems to provide value. And that might take time to develop.
Impact might also vary across the ecosystem. Suppliers of “picks and shovels” might profit in the short term even if “gold seekers” do not uniformly benefit.
Also, even if value does not appear in GDP statistics, it still is possible that revenue and profits earned by at least some companies in the AI value chain will show positive changes. Think Nvidia and other graphics processing unit suppliers, or possibly “AI as a service” revenues earned by cloud computing as a service providers such as Amazon Web Services.
As providers of infrastructure, such firms might profit even if others who purchase products and services from infra suppliers do not show revenue or profit gains in the short term.
In other words, there might be infrastructure supplier winners in the short term, even if many other entities make big investments in generative AI that do now show revenue or profit impact in the near term.
And even some who are skeptical about the magnitude of positive impact in the short term might well concede that the long term impact is going to be evident.
By way of perspective, about $5 trillion in information technology investments are made every year, according to researchers at Gartner.
source: Goldman Sachs Global Investment Research
“Generative AI has the potential to fundamentally change the process of scientific discovery, research and development, innovation, new product and material testing an so forth, as well as create new products and
Platforms,” he notes. “But given the focus and architecture of generative AI technology today, these truly transformative changes won’t happen quickly and few—if any—will likely occur within the next 10 years.”
Again, the key phrase might be “today.” Generative AI is expected by some to achieve human-level performance in most technical capabilities by the end of this decade, and compete with the top 25 percent of human performance in all tasks before 2040, according to McKinsey.
If so, both optimists and pessimists have a valid point. In the short term, gains might be muted; in the long term just the opposite could occur.
One study suggests “that around 80 percent of the U.S. workforce could have at least 10 percent of their work tasks affected by the introduction of LLMs (large language models), while approximately 19 percent of workers may see at least 50 percent of their tasks impacted,” the authors estimate.
Significantly, though, they do not speculate about the amount of time those changes will take, and when they will be realized. Again, there is a cost-benefit issue. To provide lots of value, generative AI has to prove it can address complex problems that displace high-priced labor or create other sources of value that drive growth, new products or markets.
McKinsey suggests a longer time frame as well. For specific capabilities, the timeline for achieving human-level performance has been pulled forward, compared to earlier forecasts. They suggest human level performance happening perhaps two decades earlier than previously seen:
Creativity: from around 2048 to 2023
Logical reasoning and problem solving: from around 2043 to 2023
Natural language understanding: from around 2055 to 2025
Social and emotional reasoning: from around 2050 to 2033
Still, all those developments are far outside the financial return window for capital investments to be made over the next several years, which might be expected to produce breakeven results on investment in three to five years, with gains thereafter.
The point is that operating profits from large capex programs typically are not seen in a matter of a few quarters. Granted, software firms might often expect capital investment “breakeven” points to be reached in two years or less. More capital-intensive “utility-type” firms might expect capex breakeven in two to five years.
Measurable generative AI returns should not take five years, as cost savings should be quantifiable, for some use cases, within a year or so. Measurable returns for other use cases might not be so easy, or so swift.
The ultimate results may well turn on how fast generative AI is able to prove useful for complex tasks. As always, much hinges on the assumptions we make. How much benefit will accrue from automation, and how much from faster rates of innovation?
For example, Acemoglu assumes that generative AI will automate only 4.6 percent of total work tasks, while Goldman Sachs economists estimate that generative AI will automate 25 percent of all work tasks following the technology’s full adoption.
source: Goldman Sachs Global Investment Research
“Acemoglu’s framework assumes that the primary driver of cost savings will be workers completing existing tasks more efficiently and ignores productivity gains from labor reallocation or the creation of new tasks,” say Goldman Sachs economists. “In contrast, our productivity estimates incorporate both worker reallocation—via displacement and subsequent reemployment in new occupations made possible by AI-related technological advancement—and new task creation that expands nondisplaced workers’ production potential.”
“Differences in these assumptions explain over 80 percent of the discrepancy between our 9.2 percent and Acemoglu’s 0.53 percent estimates of increases in total factor productivity over the next decade,” the Goldman Sachs authors say.
As always with forecasts, the assumptions are key. How much value, and when that value is obtained, all vary based on the assumptions.
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