WSJ on asking oddball questions in an interview: “Research has shown that people project symbolic personality traits onto organizations. “Walmart might be considered a traditional, conservative company, whereas Uber might be more stylistic,” says Zhang. “I think some companies use these oddball personality questions to stand out, to say ‘we are this cool company,’ but the practice could backfire.” “You might be losing out on talent, and you’re not getting anything in return with these oddball questions that don’t assess job-relevant traits,” Zhang says. “They may be handicapping your hiring by including biases and judgment errors.” If hiring managers really want to ask oddball personality questions to seem cool or fun, Zhang advises saving them for later parts of the recruitment process, ideally after an offer is made or the contract is signed. “Use them as an ice breaker or image tool, as long as it doesn’t interfere with the selection process,” he says. “That way, the risk of making applicants uncomfortable or turned off is no longer there, and you can reap the benefits, not the cost.”
Martin Casado: “Generative AI can bring real economic benefits to large industries with established and expensive workloads. Large language models could save costs by performing tasks such as summarizing discovery documents without replacing attorneys, to take one example. And there are plenty of similar jobs spread across fields like medicine, computer programming, design and entertainment. Consider the task of creating an image to use for marketing content or for a movie poster. For companies running their own version of an open-source model like Stable Diffusion, it costs roughly 0.1 cent and takes around one second to generate an image. Hiring a graphic designer or a photographer would cost hundreds of dollars and take hours or days. Even if, for simplicity’s sake, we underestimate the cost at $100 and the time at one hour, generative AI is 1/100,000th the cost and 3,600 times the speed of the human alternative. For generative AI to remake our economies and lives to the same degree does assume a continued pace of innovation, but many experts believe we’re very likely to see continued progress. There might be growing pains like the Hollywood strikes along the way, but the end result is more jobs, more economic expansion and better goods for consumers. This was the case with the microchip and the internet, and it will be with generative AI, too.”
Mint: “There are four main schools of political economy: The normative school with a benevolent policymaker at its centre trying to maximize a social welfare function; public choice theory with its focus on rule making; the Chicago school where policymakers redistribute wealth to maximize a support base; and the transaction cost school where each policy is a ‘play’ in a game where multiple principals try to influence an agent (policymaker) to maximize their own utility. Given our sub-optimal policy making history in the first four decades after 1947, the Indian political economy cannot be said to conform with the normative school model. One is tempted to shoehorn India into the public choice theory mould, but frequent government failures make this too ambitious. The Chicago school vision, with resources redistributed among hungry factions to shore up political support, perhaps appeals most to our tax-paying bourgeoisie. This model though ignores institutional features of a country and is not rich enough to capture the dynamics of policymaking in our complex democracy. A more apt characterization of our political economy can be achieved through the lens of Avinash Dixit’s transactions cost model. While all of us, irrespective of socio-economic group, claim to be victims of poor policymaking, in reality, we usually abet exactly that, since all socio-economic groups view policy as a chance to maximize benefits for themselves. This lends itself to game theoretic analysis.”
Ashoka Mody: “Since the mid-1980s, Indian and international observers have predicted that the authoritarian Chinese hare would eventually falter, and the democratic Indian tortoise would win the race. Recent events – China’s harsh zero-COVID restrictions, rising youth unemployment, and the adverse repercussions of the Chinese authorities’ ham-handed efforts to rein in the country’s overgrown real-estate sector and large tech companies – seem to support this view. But while China, with its deep well of human capital and greater gender equality, stands poised at the frontiers of both the old and the new economies, Indian leaders and their international counterparts tout an ahistorical ability to leapfrog over a fragile human foundation with shiny digital and physical infrastructure. China has a plausible path through its current muddle. India, by contrast, risks falling into blind alleys of unfounded optimism…Beginning in the mid-1980s, the prevailing belief among Indian and international observers was that the authoritarian Chinese regime would mismanage its economy, while democratic India would emerge as the bigger and more developed of the two. Instead, India is now paying the price for underinvesting in its human capital.”
