FT: “Social media companies make money from attention, which in practice means rewarding sensationalism and inflammatory content with little regard for truth. They have also until recently avoided liability for harmful or false information by using the defence that they are merely neutral platforms on which other people publish. In contrast, as British philosopher Dan Williams argues, AI companies are competing to serve customers who are paying for accurate, objective and, well, intelligent, tools that deliver factual information, often for business-critical purposes. When LLMs do surface harmful or dangerous content, they are on the hook. In Williams’s parlance, this makes them fundamentally “technocratising”, exerting the opposite force to social media’s radically democratising influence.”
Rama Bijapurkar: “Most plans rest on the logic of “industry is expected to grow at x per cent and, on that basis , we will grow y per cent”. Perhaps boards also need to do their bit in pushing customer centricity by asking (especially if the company is a market leader) how exactly industry growth happens and what customer-related assumptions underpin this forecast. They must also ask for growth plans to be broken down into components of price-led growth, mix (or portfolio)-led growth, and volume-led growth, and then ask “who”— not to be confused with “where” (geography) and “what” (product) — this growth will come from and why, Socratically drilling it all the way down to management’s foresight about customer behaviour. Yes, it works for all kinds of business, be they business-to-customer, business-to-business or direct-to-consumer!”
FT: “For all practical purposes, prediction markets seem to work as well as a gaggle of economic experts. However, sourcing the wisdom of crowds to help with decision-making can also lead to catastrophic failures. James Surowiecki, in his classic book The Wisdom of Crowds, explains that four criteria need to be met for a crowdsourced answer to be wise. Prediction markets are very good at two of them. They help aggregate private judgments into a collective decision and combine inputs from people with local or specialised knowledge. The remaining two criteria are where prediction markets can and do break down. In order for crowds to make more accurate forecasts than experts, there needs to be a diversity of opinion, and the opinions of the people in the crowd need to be independent of each other. These conditions are typically not violated when crowds try to predict technical issues like inflation or unemployment numbers.”
Peter Earle: “History suggests that the economic consequences of sweeping technological change hinge less on the invention than on the institutional ecosystem surrounding it. Electrification required factory redesign. The internal combustion engine required road networks and suburban development. The Internet required specialized software, new legal frameworks, and payment systems. Artificial intelligence will be no different. Its aggregate productivity impact will depend on education systems that adapt, firms that reorganize workflows, and regulatory regimes that neither stifle experimentation nor generate moral hazard. In that sense, the Productivity Panic of 2026 is likely to be less about machines replacing workers than about whether our institutions can evolve as quickly as our technologies.”
