Interview Analyst firm Forrester\u2019s vice president and principal analyst J. P. Gownder remains unconvinced that AI will revolutionize productivity.<\/p>\n
\u201cWhere we are today, we’re not seeing it,\u201d he told The Register in an interview this week.<\/p>\n
During our conversation, Gownder cited US Bureau of Labour Statistics that suggest the advent of the personal computer also did not improve productivity, which improved by 2.7 per cent annually from 1947 to 1973, but just 2.1 percent between 1990 and 2001.<\/p>\n
\u201cSo despite all those PCs, it [productivity growth] was a lot lower. And [from] 2007 to 2019 it was 1.5 percent. If you look at these numbers, productivity is the foundation of job replacement and of job growth and a whole bunch of things. But when you look at this \u2026 you begin to get the picture that information technology isn’t measured always in as linear a way into productivity as people assume. It just isn’t there.\u201d<\/p>\n
That might all change in the future if AI agents improve, but Gownder cannot find evidence that today\u2019s AI boosts productivity.<\/p>\n
\u201c[Nobel Prize winning economist] Robert Solow said that by 1987 the effects of the PC revolution can be seen everywhere, except in the productivity statistics. That holds true today as well,\u201d Gownder said. \u201cProductivity just has not soared.\u201d<\/p>\n
The absence of a tech:productivity link is known as the Solow Paradox<\/a> and is often cited when economists consider IT spending.<\/p>\n \u201cThe truth of the matter is that with the exception of 2001 to 2007 when productivity was 2.8 percent growth per year, we actually never did see the PC revolution, to the extent that you might imagine,\u201d Gownder said.<\/p>\n Forrester\u2019s most recent AI job replacement research<\/a> estimates that the technology could uproot 6 percent of jobs by 2030, or about 10.4 million<\/a>, through robotic process automation, business process automation, physical robotics and generative AI.<\/p>\n Gownder believes jobs that AI takes won\u2019t come back, as typically with job losses after economic rebounds.<\/p>\n \u201cThese jobs are lost structurally, like they’re gone for good, because they’ve been replaced. That’s not an insignificant hit to the economy,\u201d he said.<\/p>\n To determine the jobs that are at greatest risk of being erased by AI, Gownder and his colleagues considered the roughly 800 job types and 34 skills defined by the US Bureau of Labour Statistics, spoke with 200 companies, and developed a method similar to the one used by University of Oxford scholars Carl Benedikt Frey and Michael Osborne in their 2013<\/a> study that measured how susceptible jobs are to computerization.<\/p>\n \u201cWe use that to then ask \u2018Is AI good at these capabilities and these tasks?\u2019 And by building that model, what we’re able to do is to understand how strongly AI influences each of those 800-something job categories when we cross reference that with the automation potential that Frey and Osborne came to,\u201d he said. Forrester can then calculate the \u201cautomation potential\u201d for many jobs.<\/p>\n The analysts also pondered whether large organizations are capable of using AI and, if so, the technology\u2019s effectiveness.<\/p>\n \u201cA lot of generative AI stuff isn’t really working,\u201d Gownder said. \u201cI mean, enterprise, and I’m not just talking about your consumer experience, which has its own gaps, but the MIT study that suggested that 95 percent of all generative AI projects are not yielding a tangible P&L benefit. So no actual ROI. McKinsey has something like 80-something percent that don’t. It is just further context that says we’re not at a place where lots and lots of people are losing their jobs right now.\u201d<\/p>\n