> What I did say is that in a hypothetical world where Apple did invest in their own fabs, they'd have much better access to memory and could be selling machines with vastly more memory than their competitors today.

Okay, I understand your point. I've always taken it as given that vertically integrating DRAM fabs was one of many options available to any very large manufacturer with sufficient cash flow. However, I think your point was inaccurate. If Apple invested in their own fabs, they would not have better access to memory today, unless they ALSO did something quite irrational and overbuilt their fab's capacity by a factor of 2X or more. In terms of greater capacity, at Apple's massive scale of consuming more than a "one fab" amount of output, owning their own fabs vs ordering from fabs owned by others still requires pre-ordering their expected RAM needs ~3.5 years in advance. Regardless who owns the fab, no fab can make significantly more chips without that long lead time. The difference in lead time, total capacity and margin between a self-owned fab you started building 3.5 years ago and "pre-buying most of a fab's output 3.5 years in advance" is minimal enough to be virtually immaterial. This entire area of strategic analysis is broadly called "Build vs Buy", but it has more to do with financial and tax metrics around cash on hand, capex, amortization and return on capital. The problem we're focused on here is lead-time or supply elasticity (eg how quickly can we get a lot more DRAM than we thought we'd need and at what cost delta). And, at this scale, those dynamics aren't fundamentally changed whether you Build or Buy.

> Should they be doing this? I'm making no statement either way.

Given the dynamics, I assumed you weren't just stating that acting irrationally is always an option, but that doing so was a rational option strategically. But it's not. Even in your scenario it wouldn't have changed anything UNLESS you imagine Apple of 3.5 yrs ago was psychic and built two fabs for every one they actually expected to need - but that would have been insane. And if they were that insane, they could have accomplished very nearly the same thing by pre-buying 2X their expected RAM needs from their 3rd party suppliers. At Apple's scale, 3rd party suppliers are what we call 'virtually captive'. You're such a large customer, you're practically funding the supplier's capex. This is a serious strategic problem for such suppliers (but great for Apple).

> I appreciate this wisdom on one hand, but on the other it's comically misguided. You have no way to evaluate the decision process if the outcomes are not considered.

Yes, of course you consider the outcome as one data point. But I said you don't assess by the outcome, meaning we don't judge the decision process's ultimate validity only (or even mostly) by the outcome. It's just one element. The reason is that for highly uncertain decision spaces with unknowable key externals, you can have NOT just: 1. Optimal decision-making / successful outcome, and 2. Sub-optimal decision-making / failed outcome but ALSO: 3. Optimal decision-making / failed outcome, and 4. Sub-optimal decision-making / successful outcome. Understanding the ramifications of this matrix is crucial - especially the last two.

A key example, is the hypothetical where a crazy strategist consults an astrologer, pre-commits to 2X the projected need for fab output (whether by building/owning fabs directly or just indirectly by pre-buying one or more fab's entire output in advance). If the astrologer says "build 2X what you think you'll need" and there's an unforeseeable confluence of events 3 years later that causes a worldwide shortage of RAM, then if you overweight the outcome in assessing if your strategic planning process is optimal, you'd always just do what the astrologer says. In short, in high-risk domains, sometimes a sub-optimal decision-making process can randomly result in a successful outcome - but that's a spurious signal.

If I'm the Apple VP responsible for DRAM supply chain strategy, the question I might ask my demand-side forecasting team is to run a post mortem study of whether any data sources might have existed at the time we locked in our DRAM capacity commit which could have predicted the DRAM bubble with sufficient certainty that we would have made a different decision back then. However, I'm virtually certain the answer is "No". At Apple's scale and longevity, their teams on this are already very good. If the answer was "Yes", they'd already have done it. I understand you think "this AI thing was knowable 3.5 years ago" but hindsight is always 20/20. If you haven't done such rigorous forecasting and post mortem analysis in complex, high-risk domains with thousands of variables, not just 'known unknowns' but many 'unknown unknowns', with billions of dollars at stake - it's difficult to appreciate the full scope of the challenge. The trick is the question is not just a true/false binary of "AI will cause a run on DRAM." The real question is multiple choice and the choices are numbered 1 through 5,000 (at least). And "AI will cause a run on DRAM" is just 'possible future scenario' #3,462.