DNA Synthesis and Sequencing Costs and Productivity for 2025

In the run up to Synbiobeta25 I decided to update the cost and productivity curves.

Here is the prior update, with a description of what they are, and are not, and of my history in developing them. You can follow the thread backwards for comments on comparisons to Moore’s Law.

I was also asked recently to provide a opinion about the feasibility of the Human Genome Project 2 proposal, which led me to dig into the performance of the Ultima UG100 instrument. I will publish my thoughts on the HGP 2 later.

The UG 100 is a truly impressive instrument, capable of sequencing >30,000 human genomes annually at 30x coverage, with only about an hour of human hands on time to start a sequencing run. The most recent price and productivity sequencing data are based on the UG 100.

As usual, please remember where you found them.

The price per base of DNA sequencing and synthesis — reading and writing DNA — based on price surveys and industry interviews. Until recently, most synthetic genes (the red line) were assembled from short oligonucleotides (oligos) synthesized in large volumes on columns (pink line). Now genes can be readily assembled from oligos synthesized in very small volumes on arrays, though data on the usage and price of array oligos is difficult to pin down; prices for array oligos are asserted to fall in the range from $.00001 to $.001 per base.

The productivity of DNA synthesis and sequencing, measured as bases per person per day, using commercially available instruments, and compared to Moore's Law, which is a proxy for IT productivity. Productivity in sequencing DNA has increased much faster than Moore's Law in recent years. Productivity in synthesizing DNA must certainly have increased substantially for privately developed and assembled synthesizers, but no new synthesis instruments, and no relevant performance figures, have been released since 2008.

On DNA and Transistors

Here is a short post to clarify some important differences between the economics of markets for DNA and for transistors. I keep getting asked related questions, so I decided to elaborate here.

But first, new cost curves for reading and writing DNA. The occasion is some new data gleaned from a somewhat out of the way source, the Genscript IPO Prospectus. It turns out that, while preparing their IPO docs, Genscript hired Frost & Sullivan to do market survey across much of life sciences. The Prospectus then puts Genscript's revenues in the context of the global market for synthetic DNA, which together provide some nice anchors for discussing how things are changing (or not).

So, with no further ado, Frost & Sullivan found that the 2014 global market for oligos was $241 million, and the global market for genes was $137 million. (Note that I tweeted out larger estimates a few weeks ago when I had not yet read the whole document.) Genscript reports that they received $35 million in 2014 for gene synthesis, for 25.6% of the market, which they claim puts them in the pole position globally. Genscript further reports that the price for genes in 2014 was $.34 per base pair. This sounds much too high to me, so it must be based on duplex synthesis, which would bring the linear per base cost down to $.17 per base, which sounds much more reasonable to me because it is more consistent with what I hear on the street. (It may be that Gen9 is shipping genes at $.07 per base, but I don't know anyone outside of academia who is paying that low a rate.) If you combine the price per base and the size of the market, you get about 1 billion bases worth of genes shipped in 2014 (so a million genes, give or take). This is consistent with Ginkgo's assertions saying that their 100 million base deal with Twist was the equivalent of 10% of the global gene market in 2015. For oligos, if you combine Genscript's reported average price per base, $.05, with the market size you get about 4.8 billion bases worth of oligos shipped in 2014. Frost & Sullivan thinks that from 2015 to 2019 the oligo market CAGR will be 6.6% and the gene synthesis market will come in at 14.7%.

For the sequencing, I have capitulated and put the NextSeq $1000 human genome price point on the plot. This instrument is optimized to sequence human DNA, and I can testify personally that sequencing arbitrary DNA is more expensive because you have to work up your own processes and software. But I am tired of arguing with people. So use the plot with those caveats in mind.

NOTE: Replaces prior plot with an error in sequencing price.

What is most remarkable about these numbers is how small they are. The way I usually gather data for these curves is to chat with people in the industry, mine publications, and spot check price lists. All that led me to estimate that the gene synthesis market was about $350 million (and has been for years) and the oligo market was in the neighborhood of $700 million (and has been for years).

If the gene synthesis market is really only $137 million, with four or 5 companies vying for market share, then that is quite an eye opener. Even if that is off by a factor of two or three, getting closer to my estimate of $350 million, that just isn't a very big market to play in. A ~15% CAGR is nothing to sneeze at, usually, and that is a doubling rate of about 5 years. But the price of genes is now falling by 15% every 3-4 years (or only about 5% annually). So, for the overall dollar size of the market to grow at 15%, the number of genes shipped every year has to grow at close to 20% annually. That's about 200 million additional bases (or ~200,000 more genes) ordered in 2016 compared to 2015. That seems quite large to me. How many users can you think of who are ramping up their ability to design or use synthetic genes by 20% a year? Obviously Ginkgo, for one. As it happens, I do know of a small number of other such users, but added together they do not come close to constituting that 20% overall increase. All this suggests to me that the dollar value of the gene synthesis market will be hard pressed to keep up with Frost & Sullivan estimate of 14.7% CAGR, at least in the near term. As usual, I will be happy to be wrong about this, and happy to celebrate faster growth in the industry. But bring me data.

People in the industry keep insisting that once the price of genes falls far enough, the ~$3 billion market for cloning will open up to synthetic DNA. I have been hearing that story for a decade. And then price isn't the only factor. To play in the cloning market, synthesis companies would actually have to be able to deliver genes and plasmids faster than cloning. Given that I'm hearing delivery times for synthetic genes are running at weeks, to months, to "we're working on it", I don't see people switching en mass to synthetic genes until the performance improves. If it costs more to have your staff waiting for genes to show up by FedEx than to have them bash the DNA by hand, they aren't going to order synthetic DNA.

And then what happens if the price of genes starts falling rapidly again? Or, forget rapidly, what about modestly? What if a new technology comes in and outcompetes standard phosphoramidite chemistry? The demand for synthetic DNA could accelerate and the total market size still might be stagnant, or even fall. It doesn't take much to turn this into a race to the bottom. For these and other reasons, I just don't see the gene synthesis market growing very quickly over the next 5 or so years.

Which brings me to transistors. The market for DNA is very unlike the market for transistors, because the role of DNA in product development and manufacturing is very unlike the role of transistors. Analogies are tremendously useful in thinking about the future of technologies, but only to a point; the unwary may miss differences that are just as important as the similarities.

For example, the computer in your pocket fits there because it contains orders of magnitude more transistors than a desktop machine did fifteen years ago. Next year, you will want even more transistors in your pocket, or on your wrist, which will give you access to even greater computational power in the cloud. Those transistors are manufactured in facilities now costing billions of dollars apiece, a trend driven by our evidently insatiable demand for more and more computational power and bandwidth access embedded in every product that we buy. Here is the important bit: the total market value for transistors has grown for decades precisely because the total number of transistors shipped has climbed even faster than the cost per transistor has fallen.

In contrast, biological manufacturing requires only one copy of the correct DNA sequence to produce billions in value. That DNA may code for just one protein used as a pharmaceutical, or it may code for an entire enzymatic pathway that can produce any molecule now derived from a barrel of petroleum. Prototyping that pathway will require many experiments, and therefore many different versions of genes and genetic pathways. Yet once the final sequence is identified and embedded within a production organism, that sequence will be copied as the organism grows and reproduces, terminating the need for synthetic DNA in manufacturing any given product. The industrial scaling of gene synthesis is completely different than that of semiconductors.

Planning for Toy Story and Synthetic Biology: It's All About Competition (Updated)

Here are updated cost and productivity curves for DNA sequencing and synthesis.  Reading and writing DNA is becoming ever cheaper and easier.  The Economist and others call these "Carlson Curves", a name I am ambivalent about but have come to accept if only for the good advertising.  I've been meaning to post updates for a few weeks; the appearance today of an opinion piece at Wired about Moore's Law serves as a catalyst to launch them into the world.  In particular, two points need some attention, the  notions that Moore's Law 1) is unplanned and unpredictable, and 2) somehow represents the maximum pace of technological innovation.

DNA Sequencing Productivity is Skyrocketing

First up: the productivity curve.  Readers new to these metrics might want to have a look at my first paper on the subject, "The Pace and Proliferation of Biological Technologies" (PDF) from 2003, which describes why I chose to compare the productivity enabled by commercially available sequencing and synthesis instruments to Moore's Law.  (Briefly, Moore's Law is a proxy for productivity; more transistors putatively means more stuff gets done.)  You have to choose some sort of metric when making comparisons across such widely different technologies, and, however much I hunt around for something better, productivity always emerges at the top.

It's been a few years since I updated this chart.  The primary reason for the delay is that, with the profusion of different sequencing platforms, it became somewhat difficult to compare productivity [bases/person/day] across platforms.  Fortunately, a number of papers have come out recently that either directly make that calculation or provide enough information for me to make an estimate.  (I will publish a full bibliography in a paper later this year.  For now, this blog post serves as the primary citation for the figure below.)

Visual inspection reveals a number of interesting things.  First, the DNA synthesis productivity line stops in about 2008 because there have been no new instruments released publicly since then.  New synthesis and assembly technologies are under development by at least two firms, which have announced they will run centralized foundries and not sell instruments.  More on this later.

Second, it is clear that DNA sequencing platforms are improving very rapidly, now much faster than Moore's Law.  This is interesting in itself, but I point it out here because of the post today at Wired by Pixar co-founder Alvy Ray Smith, "How Pixar Used Moore's Law to Predict the Future".  Smith suggests that "Moore's Law reflects the top rate at which humans can innovate. If we could proceed faster, we would," and that "Hardly anyone can see across even the next crank of the Moore's Law clock."

Moore's Law is a Business Model and is All About Planning -- Theirs and Yours

As I have written previously, early on at Intel it was recognized that Moore's Law is a business model (see the Pace and Proliferation paper, my book, and in a previous post, "The Origin of Moore's Law").  Moore's Law was always about economics and planning in a multi-billion dollar industry.  When I started writing about all this in 2000, a new chip fab cost about $1 billion.  Now, according to The Economist, Intel estimates a new chip fab costs about $10 billion.  (There is probably another Law to be named here, something about exponential increases in cost of semiconductor processing as an inverse function of feature size.  Update: This turns out to be Rock's Law.)  Nobody spends $10 billion without a great deal of planning, and in particular nobody borrows that much from banks or other financial institutions without demonstrating a long-term plan to pay off the loan.   Moreover, Intel has had to coordinate the manufacturing and delivery of very expensive, very complex semiconductor processing instruments made by other companies.  Thus Intel's planning cycle explicitly extends many years into the future; the company sees not just the next crank of the Moore's Law clock, but several cranks.  New technology has certainly been required to achieve these planning goals, but that is just part of the research, development, and design process for Intel.  What is clear from comments by Carver Mead and others is that even if the path was unclear at times, the industry was confident that they could to get to the next crank of the clock.

Moore's Law served a second purpose for Intel, and one that is less well recognized but arguably more important; Moore's Law was a pace selected to enable Intel to win.  That is why Andy Grove ran around Intel pushing for financial scale (see "The Origin of Moore's Law").  I have more historical work to do here, but it is pretty clear that Intel successfully organized an entire industry to move at a pace only it could survive.  And only Intel did survive.  Yes, there are competitors in specialty chips and in memory or GPUs, but as far as high volume, general CPUs go, Intel is the last man standing.  Finally, and alas I don't have a source anywhere for this other than hearsay, Intel could have in fact gone faster than Moore's Law.  Here is the hearsay: Gordon Moore told Danny Hillis who told me that Intel could have gone faster.  (If anybody has a better source for that particular point, give me a yell on Twitter.)  The inescapable conclusion from all this is that the management of Intel made a very careful calculation.  They evaluated product roll-outs to consumers, the rate of new product adoption, the rate of semiconductor processing improvements, and the financial requirements for building the next chip fab line, and then set a pace that nobody else could match but that left Intel plenty of headroom for future products.  It was all about planning.

The reason I bother to point all this out is that Pixar was able to use Moore's Law to "predict the future" precisely because Intel meticulously planned that future.  (Calling Alan Kay: "The best way to predict the future is to invent it.")  Which brings us back to biology.  Whereas Moore's Law is all about Intel and photolithography, the reason that productivity in DNA sequencing is going through the roof is competition among not just companies but among technologies.  And we only just getting started.  As Smith writes in his Wired piece, Moore's Law tells you that "Everything good about computers gets an order of magnitude better every five years."  Which is great: it enabled other industries and companies to plan in the same way Pixar did.  But Moore's Law doesn't tell you anything about any other technology, because Moore's Law was about building a monopoly atop an extremely narrow technology base.  In contrast, there are many different DNA sequencing technologies emerging because many different entrepreneurs and companies are inventing the future.

The first consequence of all this competition and invention is that it makes my job of predicting the future very difficult.  This emphasizes the difference between Moore's Law and Carlson Curves (it still feels so weird to write my own name like that): whereas Intel and the semiconductor industry were meeting planning goals, I am simply keeping track of data.  There is no real industry-wide planning in DNA synthesis or sequencing, other than a race to get to the "$1000 genome" before the next guy.  (Yes, there is a vague road-mappy thing promoted by the NIH that accompanied some of its grant programs, but there is little if any coordination because there is intense competition.)

Biological Technologies are Hard to Predict in Part Because They Are Cheaper than Chips

Compared to other industries, the barrier to entry in biological technologies is pretty low.  Unlike chip fabs, there is nothing in biology that costs $10 billion commercially, nor even $1 billion.  (I have come to mostly disbelieve pharma industry claims that developing drugs is actually that expensive, but that is another story for another time.)  The Boeing 787 reportedly cost $32 billion to develop as of 2011, and that is on top of a century of multi-billion dollar aviation projects that had to come before the 787.

There are two kinds of costs that are important to distinguish here.  The first is the cost of developing and commercializing a particular product.  Based on the money reportedly raised and spent by Life, Illumina, Ion Torrent (before acquisition), Pacific Biosciences, Complete Genomics (before acquisition), and others, it looks like developing and marketing second-generation sequencing technology can cost upwards of about $100 million.  Even more money gets spent, and lost, in operations before anybody is in the black.  My intuition says that the development costs are probably falling as sequencing starts to rely more on other technology bases, for example semiconductor processing and sensor technology, but I don't know of any real data.  I would also guess that nanopore sequencing, should it actually become a commercial product this year, will have cost less to develop than other technologies, but, again, that is my intuition based on my time in clean rooms and at the wet bench.  I don't think there is great information yet here, so I will suspend discussion for the time being.

The second kind of cost to keep in mind is the use of new technologies to get something done.  Which brings in the cost curve.  Again, the forthcoming paper will contain appropriate references.

The cost per base of DNA sequencing has clearly plummeted lately.  I don't think there is much to be made of the apparent slow-down in the last couple of years.  The NIH version of this plot has more fine grained data, and it also directly compares the cost of sequencing with the cost per megabyte for memory, another form of Moore's Law.  Both my productivity plot above and the NIH plot show that sequencing has at times improved much faster than Moore's Law, and generally no slower.

If you ponder the various wiggles, it may be true that the fall in sequencing cost is returning to a slower pace after a period in which new technologies dramatically changed the market.  Time will tell.  (The wiggles certainly make prediction difficult.)  One feature of the rapid fall in sequencing costs is that it makes the slow-down in synthesis look smaller; see this earlier post for different scale plots and a discussion of the evaporating maximum profit margin for long, double-stranded synthetic DNA (the difference between the orange and yellow lines above).

Whereas competition among companies and technologies is driving down sequencing costs, the lack of competition among synthesis companies has contributed to a stagnation in price decreases.  I've covered this in previous posts (and in this Nature Biotech article), but it boils down to the fact that synthetic DNA has become a commodity produced using relatively old technology.

Where Are We Headed?

Now, after concluding that the structure of the industry makes it hard to prognosticate, I must of course prognosticate.  In DNA sequencing, all hell is breaking loose, and that is great for the user.  Whether instrument developers thrive is another matter entirely.  As usual with start-ups and disruptive technologies, surviving first contact with the market is all about execution.  I'll have an additional post soon on how DNA sequencing performance has changed over the years, and what the launch of nanopore sequencing might mean.

DNA synthesis may also see some change soon.  The industry as it exists today is based on chemistry that is several decades old.  The common implementation of that chemistry has heretofore set a floor on the cost of short and long synthetic DNA, and in particular the cost of synthetic genes.  However, at least two companies are claiming to have technology that facilitates busting through that cost floor by enabling the use of smaller amounts of poorer quality, and thus less expensive, synthetic DNA to build synthetic genes and chromosomes.

Gen9 is already on the market with synthetic genes selling for something like $.07 per base.  I am not aware of published cost estimates for production, other than the CEO claiming it will soon drop by orders of magnitude.  Cambrian Genomics has a related technology and its CEO suggests costs will immediately fall by 5 orders of magnitude.  Of course, neither company is likely to drop prices so far at the beginning, but rather will set prices to undercut existing companies and grab market share.  Assuming Gen9 and Cambrian don't collude on pricing, and assuming the technologies work as they expect, the existence of competition should lead to substantially lower prices on genes and chromosomes within the year.  We will have to see how things actually work in the market.  Finally, Synthetic Genomics has announced it will collaborate with IDT to sell synthetic genes, but as far as I am aware nothing new is actually shipping yet, nor have they announced pricing.

So, supposedly we are soon going to have lots more, lots cheaper DNA.  But you have to ask yourself who is going to use all this DNA, and for what.  The important business point here is that both Gen9 and Cambrian Genomics are working on the hypothesis that demand will increase markedly (by orders of magnitude) as the price falls.  Yet nobody can design a synthetic genetic circuit with more than a handful of components at the moment, which is something of a bottleneck on demand.  Another option is that customers will do less up-front predictive design and instead do more screening of variants.  This is how Amyris works -- despite their other difficulties, Amyris does have a truly impressive metabolic screening operation -- and there are several start-ups planning to provide similar (or even improved) high-throughput screening services for libraries of metabolic pathways.  I infer this is the strategy at Synthetic Genomics as well.  This all may work out well for both customers and DNA synthesis providers.  Again, I think people are working on an implicit hypothesis of radically increased demand, and it would be better to make the hypothesis explicit in part to identify the risk of getting it wrong.  As Naveen Jain says, successful entrepreneurs are good at eliminating risk, and I worry a bit that the new DNA synthesis companies are not paying enough attention on this point.

There are relatively simple scaling calculations that will determine the health of the industry.  Intel knew that it could grow financially in the context of exponentially falling transistor costs by shipping exponentially more transistors every quarter -- that is the business model of Moore's Law.  Customers and developers could plan product capabilities, just as Pixar did, knowing that Moore's Law was likely to hold for years to come.  But that was in the context of an effective pricing monopoly.  The question for synthetic gene companies is whether the market will grow fast enough to provide adequate revenues when prices fall due to competition.  To keep revenues up, they will then have to ship lots of bases, probably orders of magnitudes more bases.  If prices don't fall, then something screwy is happening.  If prices do fall, they are likely to fall quickly as companies battle for market share.  It seems like another inevitable race to the bottom.  Probably good for the consumer; probably bad for the producer.

(Updated)  Ultimately, for a new wave of DNA synthesis companies to be successful, they have to provide the customer something of value.  I suspect there will be plenty of academic customers for cheaper genes.  However, I am not so sure about commercial uptake.  Here's why: DNA is always going to be a small cost of developing a product, and it isn't obvious making that small cost even cheaper helps your average corporate lab.

In general, the R part of R&D only accounts for 1-10% of the cost of the final product.  The vast majority of development costs are in polishing up the product into something customers will actually buy.  If those costs are in the neighborhood of $50-100 million, the reducing the cost of synthetic DNA from $50,000 to $500 is nice, but the corporate scientist-customer is more worried about knocking a factor of two, or an order of magnitude, off the $50 million.  This means that in order to make a big impact (and presumably to increase demand adequately) radically cheaper DNA must be coupled to innovations that reduce the rest of the product development costs.  As suggested above, forward design of complex circuits is not going to be adequate innovation any time soon.  The way out here may be high-throughpu t screening operations that enable testing many variant pathways simultaneously.  But note that this is not just another hypothesis about how the immediate future of engineering biology will change, but another unacknowledged hypothesis.  It might turn out to be wrong.

The upshot, just as I wrote in 2003, is that the market dynamics of biological technologies will  remain difficult to predict precisely because of the diversity of technology and the difficulty of the tasks at hand.  We can plan on prices going down; how much, I wouldn't want to predict.

Updated, um, Carlson Curve for DNA Synthesis Productivity

It seems that productivity improvements in DNA synthesis have resumed their previous pace.  As I noted in Bio-era's Genome Synthesis and Design Futures, starting in about 2002 there was a pause in productivity improvements enabled by commercially available instruments.

According to the specs and the company reps I met at iGEM 2007, a single Febit "Geniom" synthesizer can crank out about 500,000 bases a day and requires about 30 minutes of labor per run.  It looked to me like the number should be closer to 250KB per instrument per day, so I made an executive decision and allowed that the 16 synthesizers one person could run in a day could produce 2.5 megabases of single-stranded ~40-mers per day.  This in part because there is some question about the quality of the sequences produced by the particular chemistry used in the instrument.  It was asserted by the company reps that the Geniom instruments are being adopted by major gene synthesis companies as their primary source of oligos.  Note that running all those instruments would cost you up front just under US$ 5 million, without volume discounts, for 16 of the $300,000 instruments (plus some amount for infrastructure).

The quality of the DNA becomes particularly important if you are using the single-stranded oligos produced by the synthesizer to assemble a gene length construct.  To reiterate the point, the 2.5 megabases per day consists of short, single-stranded pieces.  The cost -- labor, time, and monetary -- of assembling genes is another matter entirely.  These costs are not really possible to estimate based on publicly available information, as this sort of thing is treated as secret by firms in the synthesis business.  Given that finished genes cost about 10 times as much as oligos, and that synthesis firms are probably making a decent margin on their product, the assembly process might run 5 to 8 times the cost of the oligos, but that is totally a guess.  (Here is a link to a ZIP file containing some of the graphics from the Bio-era report, including cost curves for gene and oligo synthesis.)

One final note: the Febit reps suggested they are selling instruments in part based on IP concerns of customers.  That is, a number of their customers are sufficiently concerned about releasing designs for expression chips and oligo sets -- even to contract manufacturers under confidentiality agreements -- that they are forking over $300,000 per instrument to maintain their IP security.  This is something I predicted in Genome Synthesis and Design Futures, though frankly I am surprised it is already happening.  Now we just have to wait for the first gene synthesis machine to show up on the market.  That will really change things. 

Bedroom Biology in The Economist

I have yet to see the print version, but evidently I make an appearance in tomorrow's Economist in a Special Report on Synthetic Biology.  (Thanks for the heads-up, Bill.)  I wasn't actually interviewed for the piece, but I've no objections to the text.  There is an accompanying piece that forecasts the coming "Bedroom Biotech", a phrase they seem to prefer to "Garage Biology".  Personally, I prefer to keep my DNA bashing to the garage rather than the bedroom.  Well, okay, most but not all of my DNA bashing.

The story contains a figure showing data from 2002 on productivity changes in DNA sequencing and synthesis, redrawn from my 2003 paper, "The Pace and Proliferation of Biological Technologies", labeling them "Carlson Curves" once again.  Oh well.  The original paper was published in the journal Biosecurity and Bioterrorism (PDF from TMSI, html version at Kurzweilai.net).  It isn't so much that I disavow the name "Carlson Curve" as I want to assert that quantitatively predicting the course of biological technologies is a questionable thing to do.  As Moore made clear in his paper, what became his law is driven by the financing of expensive chip fabs -- banks require a certain payment schedule before they will loan another billion dollars for a new fab -- whereas biology is cheap and progress is much more likely to be governed by basic science and the total number of people participating in the endeavor.

Newer versions of figures from the 2003 paper, as well as additional metrics of progress in biological technologies, will be available in December with the release of "Genome Synthesis & Design Futures: Implications for the US Economy", written with my colleagues at Bio Economic Research Associates (bio-era), and funded by bio-era and the Department of Energy.

To close the circle, I should explain that the "Carlson Curves" were an attempt to figure out how fast biology is changing, an effort prompted by an essay I wrote for the inaugural Shell/Economist Writing Prize, "The World in 2050."  (Here is a PDF of the original essay, which was published in 2001 as "Open Source Biology and its Impact on Industry.")  I received a silver prize, rather than gold, and was always slightly miffed that The Economist only published the first place essay, but I suppose I can't complain about the outcome. 

"Carlson Curves" and Synthetic Biology

(UPDATE, 1 September 06: Here is a note about the recent Synthetic Biology story in The Economist.)

(UPDATE, 20 Feb 06: If you came here from Paul Boutin's story "Biowar for Dummies", I've noted a few corrections HERE.)

Oliver Morton's Wired Magazine article about Synthetic Biology is here. If you are looking for the "Carlson Curves", The Pace and Proliferation of Biological Technologies" is published in the journal Biosecurity and Bioterrorism. The paper is available in html at kurzweilai.net.

A note on the so-called "Carlson Curves" (Oliver Morton's phrase, not mine): The plots were meant to provide a sense of how changes in technology are bringing about improvements in productivity in the lab, rather than to provide a quantitative prediction of the future. I am not suggesting there will be a "Moore's Law" for biological technologies. Although it may be possible to extract doubling rates for some aspect of this technology, I don't know whether this analysis is very interesting. I prefer to keep it simple. As I explain in the paper, the time scale of changes in transistor density are set by planning and finance considerations for multi-billion dollar integrated circuit fabs. That doubling time has a significant influence on many billions of dollars of investment. Biology, on the other hand, is cheap, and change should come much faster. Money should be less and less of an issue as time goes on, and my guess is those curves provide a lower bound on changes in productivity.

I will try to have something tomorrow about George Church and Co's "unexpected improvement" in DNA synthesis capacity, as well as some comments about Nicholas Wade's New York Times story.