BioCompute正在追求一个理想的世界，在这个世界里，一美元可以购买到一百万TB的存储空间。
DNA数据存储的推广通常强调其密度和持久性。然而，位于伯克利的深科技公司BioCompute将其视为每字节成本的问题，因为这个数字关系到整个领域的生死。
这个数字之所以停滞不前，原因在于结构性问题。几乎所有人都是通过合成DNA来写入数据，逐个碱基构建新的链。当你的写入步骤是制造时，成本曲线就是合成的成本曲线，而这条曲线变化缓慢。更糟糕的是，每条链都是一次性使用的，因此介质是消耗品。你在下一次写入时需要再次支付制造成本。
BioCompute的赌注在于，如果停止制造，成本曲线会有所不同。它通过用酶标记可重复使用的模板来写入数据，而不是构建链，并通过纳米孔读取数据。模板不会被消耗。将合成过程移出关键路径，主导成本就会转移到一个你可以实际施加压力的地方。
到目前为止，该公司报告的写入成本为每兆字节1美元。它的目标是每太字节1美元。第一个是团队所展示的，第二个是重用方法旨在推动的目标。
值得关注的部分是读写耦合。大多数团队优化一侧，然后将现有的测序方法附加到另一侧。BioCompute正在调整其许可的纳米孔读取过程，以适应其自己的写入化学，因此两者是相互工程设计的，而不是简单拼接在一起。如果成本要下降几个数量级，这种契合是很大一部分原因。
对于任何关注过这个领域的人来说，诚实的未解之问包括：
- 已证明的成本是在小规模下，随着规模的扩大，经济性没有保证能够持续。
- 大规模下的读取准确性尚未得到验证，这在各个方面都是如此。
- 写入成本仍然有一个主导驱动因素，整个目标依赖于将其降低。
BioCompute背后有足够的支持值得认真对待。伯克利和斯坦福的高级学者为该公司提供建议，它已经对其写入方法进行了专利申请，并与两家美国创意工作室进行了早期的付费试点。该公司提出的主张是狭窄且可测试的：重用加上共同设计的读写堆栈可以以合成永远无法实现的方式降低每字节成本。这是值得关注的关键点。
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The pitch for DNA data storage usually leads with density and longevity. BioCompute, a deep-tech company in Berkeley, however, frames this as a cost-per-byte problem, because that is the number the whole field lives or dies on.<p>The reason that number has been stuck is structural. Almost everyone writes data by synthesizing DNA, building new strands base by base. When your write step is manufacturing, your cost curve is the cost curve of synthesis, and it bends slowly. Worse, every strand is single-use, so the medium is a consumable. You pay the manufacturing cost again on the next write.<p>BioCompute's bet is that you get a different curve if you stop manufacturing. It writes data by marking reusable templates with an enzyme instead of building strands, and it reads them back with a nanopore. The template is not consumed. Take synthesis off the critical path and the dominant cost moves somewhere you can actually push on.<p>So far the company reports a demonstrated write cost of $1 per megabyte. Its target is $1 per terabyte. The first is what the team has shown. The second is where the reuse approach is built to drive it.<p>The part worth scrutinizing is the read and write coupling. Most groups optimize one side and bolt on whatever sequencing exists for the other. BioCompute is tuning its licensed nanopore read process to fit its own write chemistry, so the two are engineered against each other rather than glued together. If the cost is going to fall by orders of magnitude, that fit is where a lot of it has to come from.<p>The honest open questions, for anyone who has watched this field overpromise:<p>- The demonstrated cost is at small scale, and there is no guarantee the economics survive as volume climbs.<p>- Read accuracy at scale is unproven, which is true across the board.<p>- The write cost still has one dominant driver, and the whole target rides on bringing it down.<p>BioCompute has enough behind it to take seriously. Senior academics at Berkeley and Stanford advise the company, it has filed on its write method, and it has run early paid pilots with two US creative studios. The claim on the table is narrow and testable: reuse plus a co-designed read and write stack can move the cost per byte in a way that synthesis never will. That is the thing to watch.