How can we improve information access effectiveness by building better indexing, retrieval, information filtering and information extraction systems by considering meaning and context? (Information Retrieval/Natural Language Processing)
Which process should be followed in order to arrive at a desired and highest-quality system artefact in the shortest time, using the least money and personnel? (Methodology)
All the graphs on this topic are out-dated, specifically because the hype-train doesn't want to acknowledge that the price of usable synthetic DNA has gone up - not down (even when balanced for inflation) - over the last 5 years.
Enzymatic DNA synthesis companies are not fixing this, or if they are, are at least 5-10 years out. The fundamentals of DNA synthesis are actually super cost effective. The raw price of a 100bp ssDNA vs an assembled gene, ready to be used in a biological context, is ~200x. Even at small-ish scale, the price for a full assembled gene from the same supplier is $125 per kilobase, while the small synthesized fragment is only $4. And they're STILL losing money on the assembled genes.
Assembling genes is really unsexy, though, so the field is kinda stuck in hyping DNA synthesis while ignoring fundamentals of DNA assembly.
Existing options are based on 100-year-old overhead trolley lines which work ok, but come with dramatic downsides in capital costs and operational limitations.
Static charging takes too much time and happens frequently due to battery size limitations. (There's a huge tradeoff between carrying a lot of battery versus payload.)
Induction efficiencies are too low, so direct contact is required at these power levels
As mining transitions to zero-emissions, these infrastructure systems require the same attention and innovation
