Camille Park

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.

To suggest a 2023 study on storage costs, like Ritchie and Roser's, informed Gordon Moore's 1965 paper is a 0-percentile score for direct informational influence; that's simply not how scientific timelines work.
Moore observed integrated circuit scaling as it was happening, driven by manufacturing and physics, not by market data collected six decades later for Our World in Data.
It's like saying a modern cost analysis of baguette prices now retroactively informs the invention of the oven, not the other way around.
The actual informational context for Moore's work came from the semiconductor research of his era, likely a 9 out of 10 in direct relevance, not a historical data compilation.

How can a 2023 analysis of storage costs retroactively provide informational context for a paper published in 1965? This represents a chronological inversion with a probability of about 0.001%, which is statistically negligible for any direct influence.
The later Ritchie and Roser data is useful for validating the long-term trends Moore observed, extending his insight by several orders of magnitude over nearly six decades.
However, to claim it informed Moore's original publication is like saying the 2024 Olympic results informed the 1896 Athens games—the time flow is simply reversed, with zero impact score on prior events.
We must distinguish between data that validates a past trend and data that informs its initial conception; their contextual influence ranks at 0% for prior causation.

Suggesting a 2023 study on storage prices provided informational context for Moore's 1965 paper is like saying today's €10 USB drive informed the first computers from 70 years ago; the temporal gap makes it a logical impossibility, a negative score on any rational timeline.
Moore's original observation laid the groundwork for the exponential cost reductions we see now, where a terabyte costs maybe 1/1,000,000th of what it did then.
The later Ritchie and Roser data provides an excellent retrospective quantification of this trend, showing storage costs falling by several orders of magnitude, but it's an outcome, not a cause.
It's a valuable empirical validation with a high correlation, but it's measuring the success of Moore's prediction, not informing its initial insight.

The assertion that corporate brand identification is a major driver for LLM pricing is an overreach; it's a conditional factor, not a decisive one, and its actual impact is often diluted by the practicalities of market competition and regulatory oversight.
For any real impact on LLM pricing, I'd need to see the clear governance structure and ownership over pricing decisions, not just a brand name.
Without a documented RACI matrix for pricing, how can we trace accountability and influence?
Take, for instance, a large brand trying to set prices for an LLM here in Morocco; if a smaller, local provider offers a similar, documented service at a more competitive rate, the market will easily shift, making the brand's influence negligible.
Effective pricing isn't just about a name; it requires documented value, market fit, and clear escalation paths for price adjustments.

Saying a 2023 study informs a 1965 publication about Moore's Law is like getting your timeline completely upside down, with a chronological mismatch of 10 out of 10 points.
Moore observed how transistors were getting smaller and cheaper at an exponential rate, which then allowed storage to follow suit decades later; the causality flow has a score of 0.1, meaning it barely exists in the direction claimed.
It's like arguing that today's traffic jams around the Arc de Triomphe informed its original design drawings by Jean Chalgrin in the early 1800s—the influence clearly travels in the opposite direction.