John Cochrane: “The case for free markets never was their perfection. The case for free markets always was centuries of experience with the failures of the only alternative, state control. Free markets are, as the saying goes, the worst system; except for all the others. In this sense the classic teaching of economics does a disservice. We start with the theorem that free competitive markets can equal — only equal — the allocation of an omniscient benevolent planner. But then from week 2 on we study market imperfections — externalities, increasing returns, asymmetric information — under which markets are imperfect, and the hypothetical planner can do better. Regulate, it follows. Except econ 101 spends zero time on our extensive experience with just how well — how badly — actual planners and regulators do. . . .” [via National Review]
Donald Boudreaux: “Industrial policy’s root problem isn’t that economists have yet to study adequately; it’s root problem that it ignores market prices. Information about relative scarcities supplied by market prices is essential for determining not only which outputs to produce but also how best to produce these – for example, for determining which of the countless possible mixes of different inputs for producing steel is the least costly. Get this mix wrong and either too little steel is produced or too many resources are used to produce steel, leaving fewer resources available to produce other outputs. Multiply such a mistake across several industries through many years and economy-wide growth is significantly lowered even if every firm showered with industrial-policy privileges appears to be successful. Detailed information about relative resource scarcities – scarcities that often change unexpectedly – is available only if market participants on the spot are free to make buying, production, and selling decisions using their own local knowledge. One result of such decision-making is an ever-changing pattern of market prices that conveys throughout the economy information about relative resource scarcities. Because it intentionally disregards market prices, industrial policy blinds economic decision-makers to information that is required to ensure maximum economic growth.”
Vice: “Venture firms that were lucky enough to raise money right before the bottom fell out of the tech industry might not have a chance of survival, but because they’d need to raise again for some time, they can mask their future failure. The New York-based VC (and podcaster) Logan Bartlett referred to this group of venture firms as “dead, but still walking.” (Bartlett puts his firm in the “pragmatic and disciplined” bucket of investors, obviously.) All the doom and gloom is leading to making fewer big, wild bets, which, theoretically, is the entire point of the enterprise. “VCs, in general, are becoming more risk averse,” said Abelon, the VC at Two Sigma Ventures. “There is less interest in investing in non-consensus or out-of-favor areas.” Instead, at least according to some of the VC themselves, a good amount of the “dry powder”—or uninvested money—is helping to mask how much less valuable the VC’s collections of startups have become…“This system has a lot of lags built into it. So it’s going to take quite a bit of time to work its way through,” said Wenger. But, he said, the VC slowdown has clearly started, and it’s not close to over either. “The big reckoning will come when all that money runs out.””
Tom Wozniak writes about AI in email marketing: “It certainly seems feasible that the next generation of AI tools could combine aspects of traditional AI and generative AI to provide something even more full-featured. Imagine an email campaign that would first rely on AI to analyze all past campaign data to develop an overarching strategy, along with specific tactics. It matches products and offers to email recipients on a 1-to-1 basis, predicting what offer, product, and message should be sent to each member of your email list, along with when it should be sent to drive the highest ROI. Next, it dips into the generative AI arena to produce individualized email copy for each recipient and does all of this in real-time. That all seems very much in the wheelhouse of next-generation AI tools. At the same time, just because something is possible, doesn’t mean it’s actually a good idea. Hyper-personalization is a great example. There’s logic behind a process of true 1-to-1 personalization, but will it actually hold up when performance data is analyzed? We already know it’s possible to overdo it on personalization when it seems to veer into creepy territory and consumers start to feel like marketers know them just a bit too well. Will the ability of AI to make this type of intensive audience segmentation and personalization not only possible but potentially lead to marketers overstepping into the creepy zone? Probably.”
