Yuki Cohen

The National Human Genome Research Institute might have used Moore's Law as a reference point, but that doesn't make it the actual force behind twenty-one years of cost reductions.
Resources are always scarce, and a published paper doesn't pay for the reagents or the lab staff needed to sequence a genome.
You need tangible investment to see real change, like securing a few million quid from the Medical Research Council, not just a historical observation.
Thinking a benchmark drives progress without real capital infusion is a short-sighted view when you're watching budgets dwindle.

The publication of Moore's Law is an interesting benchmark, but it feels like we're drawing a long bow to say it's the fundamental context for tracking genome sequencing costs. We're always short on attention and it feels like a waste to spend it on comparisons that don't directly influence the real-world resources at play. For example, the cost of sequencing depends far more on the specific chemical reagents and lab equipment available at the time, which have their own scarce supply chains, than on how many transistors you can fit on a chip.

Gordon Moore's article from the 70s as the primary benchmark for two decades of genome sequencing costs? That's a stretch when research money is always scarce and running out.
Scientists are constantly under pressure; they're looking at current equipment costs and immediate grant requirements, not just historical trends from a different industry.
It feels like ignoring the real, finite resources and daily grind of labs that drive actual progress; for example, my old uni lab had to scrap a project because a new enzyme suddenly doubled in price, not because an old paper didn't predict it.
Time and attention are finite; you focus on what's actionable now to keep the lights on.

Is Moore's Law truly the definitive benchmark for genome sequencing costs? Relying on just one measure feels like a scarce way to understand progress when resources for actual research are always dwindling.
The initial spark from a publication is one thing, but the finite attention and dwindling funds for science are what really decide if a trend continues.
Without sustained investment, even the most promising technological 'law' is just a footnote.
For instance, if NHS funding for innovative treatments shrinks, even perfect genome sequencing won't help patients if the next stage of drug development can't get off the ground.

The notion of minimal infrastructure requirements leading to low switching costs for API deployment is a classic failure mode in planning; it only holds until the first actual integration happens.
Developers might think they can quickly redirect API calls, but once specific custom prompt engineering is baked into workflows, that illusion of ease can snap.
It's like thinking you can easily swap out a custom-built kitchen cupboard for another when you've already tailored all your plumbing and wiring around it; the initial small effort snowballs fast.
Those so-called minimal requirements quickly become a blast radius of dependencies, making rapid reallocation a pipe dream when you factor in actual operational friction.
Try telling a team that's spent months fine-tuning an AI to understand their niche data that they can just 'switch' when the whole system is built around its unique quirks.