Interest rates and the timing of new production

This article studies the relation between IPO investment and the rate of interest. The 1950s and early 1960s, especially, were periods of very low real interest rates, and IPO investment was very low, with firms delaying their IPOs significantly. The authors find a qualitative difference between investment of IPO-ing firms and the investment of incumbent firms. The latter is decreasing in the interest rate, as neoclassical theory predicts. On the other hand, very low interest rates tend to discourage IPOs, and this may be why the 1950s and 1960s contained few IPOs.Interest rates ; Investments

General Purpose Technologies

Electricity and Information Technology (IT) are perhaps the two most important general purpose technologies (GPTs) to date. We analyze how the U.S. economy reacted to them. The Electricity and IT eras are similar, but also differ in several important ways. Electrification was more broadly adopted, whereas IT seems to be technologically more "revolutionary." The productivity slowdown is stronger in the IT era but the ongoing spread of IT and its continuing precipitous price decline are reasons for optimism about growth in the 21st century.

Moore's Law and Learning-By-Doing

We model Moore's Law as efficiency of computer producers that rises as a by-product of their experience. We find that (1) Because computer prices fall much faster than the prices of electricity-driven and diesel-driven capital ever did, growth in the coming decades should be very fast, and that (2) The obsolescence of firms today occurs faster than before, partly because the physical capital they own becomes obsolete faster.

Liquidity effects in the bond market

The authors find that supply risk in the market for Treasury bills adds between 10 basis points and 40 basis points to the standard deviation of the T-bill interest rate. The risk will probably increase unless the Fed expands the set of assets that it uses to conduct open market operations.Liquidity (Economics) ; Treasury bonds ; Treasury bills ; Treasury notes

Liquidity Effects in the Bond Market

Our paper reports the following two findings: 1) In monthly data, bond purchases by the Fed raise bond prices and reduce bond yields. The residual bond-supply to traders is not fully predictable, and this supply-risk adds between 10 and 40 basis points to the standard deviation of the real interest rate on T-bills. 2) The Fed's open market purchases do not raise stock prices or reduce stock returns. If anything, they raise stock returns. More generally, bonds and stocks do not co-move at high frequencies. To explain these two facts, we model the bond and stock markets as spatially separate or 'segmented'. In the model, bond purchases lower bond rates, but they do not affect stock returns, and this is consistent with both facts.

Why Wait? A Century of Life Before IPO

Firms that entered the stock market in the 1990s were younger than any earlier cohort since World War I. Surprisingly, however, firms that IPO'd at the close of the 19th century were just as young as the companies that are entering today. We argue here that the electrification-era and the IT-era firms came in young because the technologies that they brought in were too productive to be kept out very long. The model assumes that the stage before IPO is a learning period during which the firm refines the idea before committing to it at the IPO stage. The better the idea, the higher is the opportunity cost of a delay in its implementation, and the earlier the firm will have its IPO.

Interest Rates and Initial Public Offerings

We study the relation between IPO investment and the rate of interest. We model the IPO timing decision and show that the implied relation between interest rates and investment is non-monotonic, and the data support the implication. At low rates of interest firms delay their IPOs. This happens because during the pre-IPO period the firm forgoes earnings that do not matter as much at low interest rates. The 1950's and early 1960's, especially, were periods of very low real interest rates, and IPO investment was low, with firms delaying their IPOs significantly. A qualitative difference seems to exist between investment of IPO-ing firms and the investment of incumbent firms which is decreasing in the interest rate, as neoclassical theory predicts.

The Q-Theory of Mergers

The Q-theory of investment says that a firm's investment rate should rise with its Q. We argue here that this theory also explains why some firms buy other firms. We find that 1. A firm's merger and acquisition (M&A) investment responds to its Q more -- by a factor of 2.6 -- than its direct investment does, probably because M&A investment is a high fixed cost and a low marginal adjustment cost activity, 2. The typical firm wastes some cash on M&As, but not on internal investment, i.e., the 'Free-Cash Flow' story works, but explains a small fraction of mergers only, and 3. The merger waves of 1900 and the 1920's, `80s, and `90s were a response to profitable reallocation opportunities, but the `60s wave was probably caused by something else.

Why We Don't Have AGI Yet

The original vision of AI was re-articulated in 2002 via the term 'Artificial General Intelligence' or AGI. This vision is to build 'Thinking Machines' - computer systems that can learn, reason, and solve problems similar to the way humans do. This is in stark contrast to the 'Narrow AI' approach practiced by almost everyone in the field over the many decades. While several large-scale efforts have nominally been working on AGI (most notably DeepMind), the field of pure focused AGI development has not been well funded or promoted. This is surprising given the fantastic value that true AGI can bestow on humanity. In addition to the dearth of effort in this field, there are also several theoretical and methodical missteps that are hampering progress. We highlight why purely statistical approaches are unlikely to lead to AGI, and identify several crucial cognitive abilities required to achieve human-like adaptability and autonomous learning. We conclude with a survey of socio-technical factors that have undoubtedly slowed progress towards AGI

Concepts is All You Need: A More Direct Path to AGI

Little demonstrable progress has been made toward AGI (Artificial General Intelligence) since the term was coined some 20 years ago. In spite of the fantastic breakthroughs in Statistical AI such as AlphaZero, ChatGPT, and Stable Diffusion none of these projects have, or claim to have, a clear path to AGI. In order to expedite the development of AGI it is crucial to understand and identify the core requirements of human-like intelligence as it pertains to AGI. From that one can distill which particular development steps are necessary to achieve AGI, and which are a distraction. Such analysis highlights the need for a Cognitive AI approach rather than the currently favored statistical and generative efforts. More specifically it identifies the central role of concepts in human-like cognition. Here we outline an architecture and development plan, together with some preliminary results, that offers a much more direct path to full Human-Level AI (HLAI)/ AGI