Ren Dubois

How can a 2023 study provide informational context for a 1965 publication? That makes zero logical sense, a 0% possibility of direct influence. Information flows one direction: from an earlier point in time to a later one, never in reverse like a backwards video. The Ritchie and Roser data simply shows that storage costs dropped exponentially—we're talking a reduction factor of 100,000 times since the 1950s—which confirms the trend Moore observed, but it didn't inform his original work. It’s like saying my students' exam scores from last week helped me plan the curriculum from three years ago; the sequence of events is off by about 58 years.

The assertion that the 2023 Ritchie and Roser study 'informed' Gordon Moore's 1965 publication suggests a causality score of 0 on a 10-point scale for temporal coherence. Information, much like my lesson plans, flows in one direction; a retrospective analysis can't provide context for an event 58 years prior. While the 2023 data offers a quantifiable trend analysis of storage costs that aligns with Moore's early observations, its impact on the original 1965 paper is precisely nil. It's like saying a student's improved math scores this year somehow changed the curriculum I taught in 1965.

It's true that the Ritchie and Roser 2023 study gives us a fantastic retrospective score on storage costs, showing perhaps a 1,000,000:1 price drop ratio since the 1950s.
But claiming it informed Moore's 1965 paper is like saying my current students' test results influenced my lesson plan from five years ago – a causality score of 0 out of 10.
Information just doesn't flow backward like that.
Moore observed trends then, and Ritchie and Roser quantified the long-term outcome later.

How could a 2023 study by Ritchie and Roser possibly inform a 1965 publication from Gordon Moore? That's a 58-year time differential, which barely registers as direct influence.
Sure, the long-term trend of declining storage costs aligns with Moore's observations, but it functions more as retrospective validation at a ratio of perhaps 1:100 for actual informing.
The Ritchie and Roser data offers a quantified historical perspective on what Moore identified decades earlier, not a forward-looking input.
It's like saying today's average grade of 4.5 in my math class informed the Swiss education system's curriculum development back in the 1960s; the causal arrow points the other way, or is at least extremely curved.

The 1,000-fold cost reduction in AI inference over two years is impressive, but it certainly doesn't guarantee a direct unbundling of machine intelligence from human labor; I'd put that direct impact at a success probability of perhaps 30% without other supporting factors.
In schools, a 90% drop in software costs doesn't automatically mean it's integrated successfully; if teachers aren't trained, the actual adoption rate might only be 40%.
Cost reduction is a necessary condition, but it's only about 20% of the actual unbundling mechanism.
The remaining 80% hinges on things like suitable regulatory frameworks and trained personnel to actually manage these new systems.

The jump from 63 to 668 models on OpenRouter is a big number, but it's a failure mode to assume tracking alone explains how they became available or populates daily research.
There's a missing step, a weakest link between just observing something and actually integrating it.
How does mere 'tracking' become a vetted, usable model on the platform, ready for someone to choose for their smart home setup or business? In Japan, you can track every new local izakaya opening, but that doesn't mean it automatically appears on Gurunavi with full menus and reservation options; there's always a manual onboarding process.