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Rob Ready @robready.bsky.social · 24/09/2026

Remember when the Tesla valuation case was that their cameras were out collecting real-world data? Every top academic in the world is currently furiously typing their most cutting-edge thoughts into AI chat windows.

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Rob Ready @robready.bsky.social · 19/09/2026

We also study extinction, as in Chow, Halperin, and Mazlish (2026). It reduces adoption, but only modestly in our examples. The effect operates through physical survival probabilities, without the risk-neutral amplification of survivable losses. 5/5

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Rob Ready @robready.bsky.social · 19/09/2026

We find a similar amplification with standard preferences over consumption. The disaster reduces dividends and consumption, including adopters’ dividends. The hedge comes from the link between capability and danger, not from adopters escaping the damage. 4/5

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Rob Ready @robready.bsky.social · 19/09/2026

The logic echoes results in climate finance. Pástor, Stambaugh, and Taylor (2021) and Baker, Hollifield, and Osambela (2022) show how hedging demand can discourage or encourage polluting investment, depending on which assets pay off when environmental damage is high. 3/5

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Rob Ready @robready.bsky.social · 19/09/2026

If capability raises both productivity and danger, disasters are more likely in states where adopters are relatively productive. Investors value that relative payoff in bad states, encouraging adoption. More adoption then creates more disaster risk. 2/5

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Rob Ready @robready.bsky.social · 19/09/2026

New paper with Roberto Gutierrez, “Do Markets Discipline the Adoption of Dangerous Technology?” papers.ssrn.com/abstract=747... When technological disasters hit adopters and non-adopters equally, markets can reward adoption precisely because it creates disaster risk. 1/5

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Rob Ready @robready.bsky.social · 21/01/2026

12/12 Taken together, our results suggest that further positive shocks to expectations about AI productivity will not “burst” an AI “bubble”.

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Rob Ready @robready.bsky.social · 21/01/2026

11/12 We also present empirical evidence showing that the characteristic negative correlation between stock prices and cash-flow expectations that is the core of the mechanism was not present in the dotcom bubble and does not appear to be present with AI stocks.

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Rob Ready @robready.bsky.social · 21/01/2026

10/12 In contrast, when adoption can be undertaken at an optimal time, positive productivity shocks incrementally bring you closer to an expected adoption, smoothing out the discount rate effect instead of concentrating it at the end of the revolution, so that there is no “bubble”.

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Rob Ready @robready.bsky.social · 21/01/2026

9/12 In the simple model, adoption occurs if tech productivity is above a fixed threshold at the pre-specified date. So, near the threshold, an arbitrarily small productivity shock can tip you from “never adopt” to “immediately adopt” and create a large effect.

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Rob Ready @robready.bsky.social · 21/01/2026

8/12 The basic intuition is that, for the discount rate effect to dominate the cashflow effect, you need small cashflow shocks to generate large changes in the expected time to adoption.

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Rob Ready @robready.bsky.social · 21/01/2026

7/12 We then derive a simple approximation for market prices that applies to both models and show that the bubble pattern is a result of the simplifying assumption of an exogenous adoption time. It is not a natural feature of the more realistic model.

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Rob Ready @robready.bsky.social · 21/01/2026

6/12 We confirm the result in PV that both models produce a hump-shaped pattern in M/B ratios. However, we show that only the simple model produces a bubble in tech stock prices. There is no ex post stock price bubble in the more realistic model.

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Rob Ready @robready.bsky.social · 21/01/2026

5/12 There are two models in PV: (i) A simplified model where the adoption of the technology is an all-or-nothing decision at an exogenously fixed date. (ii) A more realistic model where the technology can be optimally adopted at any time.

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Rob Ready @robready.bsky.social · 21/01/2026

4/12 Early in the revolution, adoption is unlikely, and the cash-flow effect dominates. Later, adoption becomes likely, the discount-rate effect dominates, and positive cash-flow shocks become negative price shocks and the “bubble” bursts.

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Rob Ready @robready.bsky.social · 21/01/2026

3/12 The mechanism in PV is elegant. New technologies that are adopted ex post, experience a series of positive productivity shocks that 1) raise expected cash flows → higher prices 2) raise adoption probability → higher systematic risk → higher discount rates→ lower prices

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Rob Ready @robready.bsky.social · 21/01/2026

2/12 We revisit the classic paper of Pastor & Veronesi (2009) (PV) and argue that the mechanism in this paper is unlikely to explain bubble-like patterns in technology stock prices, including AI.

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