# KKR's Ming Lu Says AI Spending Keeps Outrunning Any Payoff, and History Says That's Normal > Ming Lu, a senior advisory partner at KKR and the firm's former Asia Pacific chairman, argues that every major wave of business technology has sped up before it ever paid off. His read on the current AI rollout: the bottleneck was never the software. - Source: Media Broker Daily - Canonical URL: https://news.mediabroker.org/article/kkr-s-ming-lu-says-ai-spending-keeps-outrunning-any-payoff-and-history-says-that-s-normal - Author: Media Broker Daily Editorial - Section: Technology - Published: 2026-10-11T22:50:58.386Z - Updated: 2026-10-11T22:50:58.386Z - Tags: Ming Lu, KKR, artificial intelligence, productivity, enterprise AI, economic growth --- Ming Lu has spent enough years inside KKR, most recently as executive chairman of its Asia Pacific business, to have watched several technology cycles promise a productivity windfall and then take years longer than advertised to deliver one. In a recent analysis flagged by Fortune, he lays out why he expects the current AI buildout to follow the same arc: a burst of spending and excitement up front, followed by a long, unglamorous stretch before it shows up in company earnings. ## A pattern he's seen before Lu's framing is blunt: innovation and growth don't move together, they trade off against each other in the short run. He traces the same lag through the personal computer era and the smartphone boom, both of which reshaped how companies operated well before the productivity statistics caught up. His argument is that AI isn't an exception to that pattern, it's the latest instance of it. ## The numbers behind the stall The data points Lu cites are stark. In McKinsey's 2026 survey work, 88 percent of CFOs say AI has not meaningfully moved their company's bottom line, and only 6 percent of organizations have reached what the survey calls high performer status, meaning AI is adding at least 5 percent to EBIT. The gap shows up on the adoption side too: more than half of U.S. adults, 53 percent, now use AI tools in some form, but enterprise deployment sits at roughly 10 percent. Consumers raced ahead. Companies did not. ## The overhead trap Lu points to a longer-running structural problem that predates AI entirely. Looking across S&P 500 companies by sector and by decade, he finds that 73 percent of the time, overhead costs, selling, general and administrative expenses, rise even as revenue shrinks. He bundles that corporate overhead together with government spending and compliance costs into what he calls a Combined Overhead Ratio, which he estimates crossed 47 percent of U.S. GDP around the year 2000. In his telling, GDP growth hasn't cleared 2.5 percent since, and no amount of new software fixes a cost structure problem that large on its own. ## A faster engine bolted onto the same old process The core of Lu's argument is that most companies are buying AI the way they'd buy a tool, not the way they'd rebuild a factory. He describes the typical deployment as dropping a faster engine into a horse-drawn carriage: the new capability sits on top of workflows, approval chains and org charts that were never redesigned around it, so the horse, not the engine, still sets the pace. Getting the economic payoff, in his view, requires rebuilding the carriage, not just swapping the engine, which is a slower and far less visible project than buying the software in the first place. ## Why marketers and media buyers should read this as a timeline, not a verdict For an audience that sells and buys media against AI-driven efficiency claims, Lu's numbers are a useful check on how those claims get pitched. Vendor decks tend to describe AI's return on investment as a near-term, almost automatic outcome. Lu's data says the honest version is closer to a multi-decade adoption curve, gated less by model capability than by how much of an organization is actually willing to rewire itself around the tool. Deep penetration inside individual companies and broad adoption across entire sectors both have to happen before the growth numbers move, and Lu's own estimate is that this is a process measured in decades, not product cycles. --- Originally published by Media Broker Daily. Free to cite with attribution and a link to https://news.mediabroker.org/article/kkr-s-ming-lu-says-ai-spending-keeps-outrunning-any-payoff-and-history-says-that-s-normal.