Without doing the math to check, nothing that you said seems wrong. However, I take a very different lesson from the idea of the Lindy Effect than you do. Specifically, the Lindy Effect tells us that when making predictions about future lifetimes of non-perishable things, we should assume a power law distribution. If you've never dealt with predictions under thick-tail assumptions, you might be surprised how little intuition you will have for it. (The 80-20 rule is another example of assuming thick-tails.)
For example, a Pareto distribution (the easiest of the power law distributions) will typically have a mean larger than the median, and the mode (most common single outcome) is essentially instant failure. If the constant of proportionality in the Lindy Effect is greater than one, this implies an infinite variance even with a finite mean. Also, the force of mortality (instantaneous rate of death, aka hazard function) is a decreasing function of time.
The reason that this is typically a good rule of thumb for making future estimates is a consequence of making predictive distributions (i.e. integrating out uncertainty in a fit parameter). As the most familiar example, remember that even if the maker of the Matrix came down and told that a random variable was normally distributed, you will still need to estimate the parameters from observation. This will lead to your mean estimates being distributed as a Student-t, which is a power law.
If I'm reading you right (low confidence), then I think our lessons are compatible. The longer something's been around, the longer we should expect it to continue, in absolute terms. At the same time, our best outside view guess is always that the thing is getting toward the end of its life, in relative terms.
I notice the Lindy Effect getting tossed out often as a counterargument to an inside view. So for example, if John says "the Catholic church is on its last legs," Alice might say "it's been around for almost two millennia... (read more)
From Wikipedia:
There are a some shallow criticisms you could make of it as a predictive tool. How do you define the thing? What's the proportion of age to life expectancy? How often do we update it?
But these are all problems of refining a loose claim into an unambiguous prediction. How would we evaluate whether this heuristic is a useful predictive tool?
Let's imagine that we're updating the Lindy effect predictions every year.
Bathing machines were roofed and walled wooden carts rolled to the beach to allow swimmers to wash and change in privacy. The earliest evidence of them is an engraving from 1735, and they were almost entirely out of use by the early 1920s. Their technological lifespan is around 185 years.
Let's be generous to the Lindy effect, and say that it needs to give a lifespan estimate that's within 25% of the true value to count as correct. In this case, it needed to predict that bathing machines would become obsolete sometime between 1874-1966.
People often use the Lindy Effect to predict that at any given time, we're halfway through the lifespan of anything that currently exists. Using that value, it generated correct predictions between 1805 and 1851, about 25% of the lifespan of the bathing machine. So across the lifetime of the bathing machine, the Lindy Effect was usually wrong.
To take this a step further, I wrote a Python script that finds the most successful Lindy Effect proportion. The code is in the comments. Here are my results.
Estimating that the bathing machine would be around for 125% of its present age would therefore have worked from 1846-1920.
What are the takeaways? Even interpreted very generously, the Lindy Effect is usually wrong. It's most successful when it estimates a thing has "one more phase of life" left; analogous to entering retirement. It's only accurate during the last 40% of the thing's actual lifespan.