ChatGPT has been an overnight sensation in the world of internet dependants — most of us, that is. But as any overnight sensation would attest, it takes a lifetime of sacrifice and investment to become that overnight sensation.
Huge investments are being made in developing and applying generative artificial intelligence (AI), and the great IT companies are leading the way. Much of their heavy R&D is in the form of employment benefits for armies of researchers — increasingly applying AI — to answer the questions customers and colleagues ask them more effectively and rapidly.
Among the important applications of AI is in the writing of the code that animates software and its development. The R&D is mostly expensed through the income statement and may not appear on balance sheets, but it will attract great value from the investors who determine the share price of the IT giants that dominate the market value of the S&P 500. Understandably so, given the promise of AI.
Perhaps the most important question shareholders should now be asking their managers is how they are adapting to AI. It is estimated that a fifth of office workers’ time is spent answering inquiries of some kind. Imagine AI as that true expert on the customer or the internal functions and operations of your company, always sitting beside you and your laptop and comfortably speaking and understanding your language.
You will have clear, immediate answers to these questions. Better still, the expert may help them ask better, more imaginative and important, questions, the answers to which will follow. It is asking the right questions that lead advances in science. Humans will be needed for that.
Productivity increase
McKinsey has attempted to measure the potential of AI from the bottom up, so to speak, by examining in detail how AI is adopted in the workplace. It has come up with imposing estimates of extra GDP and faster rates of change of output and productivity — output volumes divided by the number of work hours producing them.
To quote Bloomberg on the McKinsey study: “Whole swathes of business activity, from sales and marketing to customer operations, are set to become more embedded in software, with potential economic benefits of as much as $4.4-trillion, about 4.4% of the world economy’s output ... Depending on how the technology is adopted and implemented, productivity could increase 0.1%-0.6% over the next 20 years”.
A follow-up question is worth asking. How well will the growth in productivity show up in the numbers we use to measure output and productivity and its growth? One of the puzzles economists have been wrestling with for years is the apparently persistently slow growth in productivity, despite conspicuous automation and labour saving. We are almost certainly entering a new phase of productivity improvements, but to recognise them we will need superior measurement techniques. Perhaps AI will help.
Inflation adjustment
We measure the value rather than the volume of production. Revenues recorded are prices charged in money of the day, multiplied by the quantity of goods and service supplied. This is easier to measure in mines, farms and factories than in the increasingly predominant service sector of a modern economy. We compare company revenues now with revenues one or 10 years ago, when prices observed were generally lower, given inflation.
Yet to make comparisons of real output and income and their growth over time, the value of all the goods or services supplied has to be adjusted for inflation. And, most importantly, also for the changes in the quality of the presumed volume of goods and services supplied.
Underestimate quality gains incorporated into the price of goods and services and you will overestimate inflation by a percentage point or two a year, and you will be underestimating productivity gains and economic growth at the same rate — and then be telling a quite different story about economic progress.
• Kantor is head of the research institute at Investec Wealth & Investment. He writes in his personal capacity.
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