[D]ata are seen to enable modes of action that are meant to ofer efective ways to respond to those problems. With more data, potentially more accurate data, and more extensively distributed data, environmental problems such as air pollution are intended to be more readily and efectively addressed. Data are intertwined with practices, responses to perceived problems, modes of materializing and evidencing problems, and anticipations of political engagement.27

By measuring environmental data of human concern, with the help of the humans who are afected by it, and then creating human-legible data stories, Citizen Sense makes visible the unseen, the ignored, and

25 For more, see Brian Handwerk, 'With wearable devices that monitor air quality, scientists can crowdsource pollution maps', *Smithsonian Magazine* (March 12, 2015), http://www.smithsonianmag.com/innovation/with-wearable-devicesthat-monitor-air-quality-scientists-can-crowdsource-pollution-maps-180954556/ #5zKIohrRUEkmuLlr.99.

26 See also Davey Alba, 'This wearable detects pollution to build air quality maps in real time', *Wired* (November 19, 2014), https://www.wired.com/2014/11/ clarity-wearable/; Megan Treacy, '10 environmental sensors that go along with you', *Treehugger* (March 5, 2013), http://www.treehugger.com/clean-technology/ environmental-sensors.html; 'Small, portable sensors allow users to monitor exposure to pollution on their smart phones' (n.a.), *Jacobs School News, UC San Diego* (December 18, 2012), http://jacobsschool.ucsd.edu/news/news\_releases/ release.sfe?id=1295; and visit Instructables to fnd instructions to make your own DIY sensor: FabLabCuneo, 'Environmental pollution', *Instructables* http://www. instructables.com/id/Environmental-Pollution/. As of early 2017, the United States EPA website even has an 'Air Sensor Toolbox' for citizen scientists: United States Environmental Protection Agency, *Air Sensor Toolbox for Citizen Scientists, Researchers and Developers*, https://www.epa.gov/air-sensor-toolbox.

27 Jennifer Gabrys, *Program Earth: Environmental Sensing Technology and the Making of a Computational Planet* (Minneapolis: University of Minnesota Press, 2016), p. 159, https://doi.org/10.5749/minnesota/9780816693122.001.0001.

the overlooked. If, as Gabrys asks 'environmental problems need to be visible in order to be actionable', these projects are revealing untold stories and facilitating participation—both bodily and narratively between humans, pollution and the environment.28

The ecocritical digital projects mentioned here allow us to read air, birds, marine animals and environmental health as environmental texts through digitally-mediated collaboration. These are perhaps precarious, imperfect readings but they are, in a way, entry points, or 'ecotones', if we might borrow a term from ecology and permaculture, that blur the boundaries between human and nonhuman and environment. If we respond by making moves to clean, restore and protect those who and that which—speak to us through data, these ecocritical digital projects are indeed planting seeds. They open, as Gabrys says, 'new ways of approaching digital technology as material, processual, and more-than-human arrangements of experience and participation' while all the while also opening new ways of approaching our environments as material, processual and more-than-human arrangements of agencies.29 In bringing an ecocritical humanities lens to environmental issues, these projects complicate the complex relationship between humans and animals and illustrate the fragility of the claim for humannature separation and the damage done by what Plumwood calls hyperseparation.

Projects like *Soil Selfes* and *FutureCoast* combine the mediated digitality of Sample's sharks with the human-involvement of Gabrys's Citizen Sense projects to create wholly new experiences. The goal of the 2015 *Soil Selfes* project, developed by Australian environmental educator Jeanie Clark, was to coerce people to develop a new, and more caring and familiar, relationship with the soils upon which they walk and from which their food feeds.30 Participants were invited to take and post photos of themselves with their soils and to thus take a moment to consider how intimately associated their lives are with our soils. Soil provides food, plants (which clean the air), natural environmental protection like water barriers, human and nonhuman

28 Ibid., p. 138.

29 Ibid., p. 21.

30 See the *Soil Selfes* project here: enviroed4all, *Soil Selfes* (July 12, 2015), http://soils. enviroed4all.com.au/soil-selfes/.

habitat, minerals, raw materials, water fltration and even carbon storage. Soils are our friends, with whom we can take selfes, and *Soil Selfes* celebrates this.

*FutureCoast* is a more robust project devised by *World Without Oil* designer Ken Eklund and a team at Columbia University. Funded by a National Science Foundation grant, *FutureCoast* is a storytelling project that asks participants to record voicemail messages from the future. Those voicemails are then organized, published, and promoted on the *FutureCoast* website. Recognizing that science has not yet been able to tell compelling behaviour-altering stories of climate change and Anthropocene futures, *FutureCoast*'s simple intervention adds the missing human dimension. The human voice replaces scientifc data and the contemporary medium of voicemail message brings the future into the present. Chilling quick messages left by friends, family members and neighbors report of water shortage, underground markets for fsh or fruit, carbon rationing and extreme weather events:31

'Hi it's me. the city has just turned of the water. If you've got water, bring home as much as you can. Love you, bye'.

'Hi Mom. I'm calling to see if you or grandpa happen to have any credits left on your Cal Card for the month. I was [robbed] and they got my Cal Card. And we're running low on food here'.

'Oh hey, Jordan, this is Dave. The house battery isn't charging like it used to. I even hooked it up to the bicycle recharger and it's just not holding a charge anymore. Do you think you can come by and take a look?'

Other messages, from automatic robotic messaging systems, foretell eminent institutional organizations:32

'Good morning valued citizens, this is your monthly reminder from the Department of Resource Conservation and Rationing. Remember that your water usage is limited. Each family is allowed two gallons a day. Failure to comply will lead to disciplinary action and potential deportation'. (Automated Human voice)

31 All messages taken from *FutureCoast*.

32 *FutureCoast*.

'Hello, this is the Federal Department of Rations reminding you to renew your sixth-month order of rations. We thank you for your cooperation. Have an eco-friendly day'. (Robot Voice)

These glimpses from the future remind us of our present. The voices compel us to rethink our relations to water, oil, food and our resourcesharing human and nonhuman kin. They ask us to think about what we would we do if we didn't have water to grow our last cucumber seeds or bathe before work. They introduce us to short tales of woe due to rationing, scarcity, surprise weather events, and property value loss due to climate change. The voicemail messages, with diferent voices and various qualities of transmission (like real voicemail messages) put us on the phone with the future.

Eklund reports participants in his globally-known *World Without Oil* project—which created a simulated no-oil world, complete with an infrastructure of news updates, mock protests, and live participant feedback to make the situation more realistic and asked participants to live as if the world was experiencing an inescapable oil shortage actually succeeded in transforming participants' lifestyles. After being immersed in the alternate realty of a world without oil, participants planted gardens, started cycling to work, and adopted other oilsaving practices. The hope, of course, is that *FutureCoast* participants and listeners will do the same. As an ecocritical project, it uses digital technology to refect on a diminished present and future habitat in part propelled by our excessive overreliance on polluting material-intensive technologies.

Ecocritical digital humanities projects, like these mentioned here and in other contributions in this volume, can reconfgure our perceived contingencies. They re-narrate through digital tools and hands-on practice, reframing, providing 'proof', collaboration, ecodata and digital stories. Ecocritical digital work recognizes our technospheric condition and disturbs it, positively, through education, outreach, participation and cultivation of new fows of behaviours with the system.

The point is not to advocate we rid ourselves of technology—after all, man is both ecological and technological, as Claire Colebrook reminds us—but to add a layer of metabolic awareness to our technology use and theory and to use our technologies to address the issues they create.33 We have an opportunity to set an example from within our own digitallyinfected humanities felds of praxis, and we have a ready platform for using our knowledge to help foster lasting changes of practice and relational understanding.

#### Bibliography

100 Views of Climate Change, http://changingclimates.colostate.edu/

Climate Stories NC, https://climatestoriesnc.web.unc.edu/

33 Claire Colebrook, 'Not symbiosis, not now: Why anthropogenic change is not really human', *The Oxford Literary Review*, 34.2 (2012), 185–209 (p. 195), https://doi. org/10.3366/olr.2012.0041.

# 5. Impact of the DigitalRevolution on Worldwide Energy Consumption

*Doug Barlage and Gem Shoute*

We Tweet, Facebook, Netfix and YouTube in the palm of our hand. We are aware of the amount of energy that it takes from how many times that we need to recharge our devices. However, this is just the tip of the iceberg. For every joule of energy we expend locally, many more joules are spent in the backbone of the Internet. While our appetite for data has largely been insatiable over the last thirty years, the energy required to sustain this has been held in check by Moore's Law's driving creed that density of function in a computer chip increases by two every two years, and energy/function decreases by a similar amount. With that said, this driving relationship between power consumption and computing density is slowing due to a multitude of physical constraints when the density of transistor packing approaches the limits. In the following chapter, the authors examine these relationships and outline some of the challenges that the world is facing as we continue to meet and exceed the expectations of our data-driven world with a fnite growth in worldwide power generation capacity.

### Introduction

Increases in computational demand has led to rising demands on the power grid for energy efciency. When we use our cell phone, our computer or one of the many things that interact with the larger cloud of data, we forget that we are exercising a certain amount of energy to do so. Unlike a light bulb where the energy spent is all at the point of use, when we watch a viral cat video on our phone, we spend about 10–100 times more energy supporting the data path to get it to you. That energy is spent away from you, but nonetheless it is required so that you can download information and fully enjoy the fruits of the information age. For that matter, the conference on sustainability that inspired the initial presentation documented in this paper depends on the World Wide Web. Streaming information across the planet forces energy to be expended. Whether it is watching Netfix or researching remote documents to write a paper, the computations required are not an amount that can be brushed aside as trivial. While this energy to communicate across the Web is considerably smaller than the amount of energy that it would take to bring everyone physically to the same room at the same time, it is not zero and in our growing, increasingly interconnected world, it can no longer be considered an afterthought of the total energy that we expend in our daily lives. Computational energy has become something that must be considered when planning on future energy demands. Our increasing demand for instantaneous information consumes energy that is defnitely not zero and is actually the fastest growing area of energy consumption around the world. In this chapter, we introduce the reader to the basic principles of energy consumption by digital computation and the limitations in reduction of this energy using the current technology available. The realities of Moore's Law reaching a plateau are discussed. We follow this with a calculation of the demand that is occurring on a rapid level as the information revolution continues to unfold before us, and we identify the limiting economic principles that drive this energy consumption phenomena. Lastly, we propose some approaches to fnding a solution to this dilemma and look at the role that industry and national governments are playing and should play as this increasingly demanding economic sector continues to require an ever-growing quantity of energy.

#### Energy Consumption of Computation

Computation machines preceded the discovery of the transistor by John Bardeen and Walter Brattain by several thousand years.1 The abacus was one of the frst computational machines in existence. The abacus served the ancients through the accountants and traders that used it. The energy consumption of these abacuses should be thought of as two distinct entities: the actual energy used to drive the abacus and the energy used to sustain the driver of the abacus. This could be considered the energy consumed by the system. In the days of the ancients, this would be considered the amount of food required to feed the accountants who drove the abacus. We refer to this as *ESystem*. Today this energy is the amount of energy that our computational machines require. Furthermore, it is possible to directly analyze the amount of energy required to do the computation. The energy required to do a computation could be determined by the amount of energy required to move the beads. This energy is of the non-recoverable variety and could be easily calculated if the amount of friction was known. In a very general way, the energy could be calculated as follows:

$$E\_{\mathcal{C}omputation} = \int\_{X\_{\text{start}\sigma}}^{X\_{\text{start}\sigma}} F(\boldsymbol{x})d\boldsymbol{x}$$

Where the force, *F(x)*, is the force required to move the beads and *XState1*  is the starting position and *XState1* is the ending position. A frst-year physics student is taught to make this calculation. To try and make this a little more tangible, we can look back to the Babbage machines of the 1800s. Modern computational apparatus and architectures are at least an inspirational descendent of these devices. It is possible to see what's going on the inside with your eyes rather than relying on electron microscopes and what really amounts to applied imagination when you look at the working mechanisms of a modern system. Every piston that moved to perform a calculation expended an amount of energy that is easily observed. This energy is of the kind that follows thermodynamic principles of non-recovery and introduces some

1 John Bardeen and Walter Hauser Brattain, 'The transistor, a semi-conductor triode', *Physical Review*, 74.2 (1948), 230–231, https://doi.org/10.1103/physrev. 74.230.

entropy. These systems had hundreds of tiny pistons that executed algorithms at several calculations per second. To perform a binary calculation many pistons would have to change positions many times to perform one operation. It might be said that our mechanical computation forerunners had a superior way to do calculations. It was able to accomplish more output with less internal operations. In modern terms, this is referred to as a foating point operation or FLOP. Our modern systems require that many individual beads must change position to perform one calculation.

In principal, the driving notion behind reducing energy in computation from an engineering standpoint remains essentially the same today. But instead of beads we move electrons from one energetic state to another. In a very tangible, though not computationally sufcient way, reductions in energy on the per bit level comes from moving states closer together with lower amounts of energy between those states. Using these ideas, we can calculate the efciency of the system and put this directly in the context of power and bits per second. While the modern computational system is a little more complicated than an abacus, the principle is the same. A user tells the computational system what it wants and the computational system does what it takes to provide the user what it desires. Every time the computational system moves a bead on the abacus, the computational system takes energy. A modern system such as your cell phone has well over a billion such beads that calculate at well over a billion calculations per second. When the calculation system does its work, it takes energy. Some of the energy is used to move the beads on the abacus, and other energy is required just to maintain the computer. You have to supply both to make the whole system work and keep it healthy and so this has to be included when you consider energy consumption. Just as if we had a million accountants from ancient times to do our bidding, we have to feed those accountants to keep them well and we have to feed them the energy it takes to do our bidding. Likewise, we feed our computers to allow them to do work.

It was not until after World War II that vacuum tubes powered the frst electronic computers. The frst system, ENIAC, was constructed at the University of Pennsylvania, consumed about 150kW and ft in a fair-sized room. For reference, 150 kW is pretty close to the power that a modern electric vehicle expends when it is accelerating to cruising speed. This is something that is completely manageable by today's standards, but in 1946, when the only real loads on the grid were light bulbs, heaters and refrigerators, this was a signifcant amount of energy. The frst computer on the campus at the University of Pennsylvania took about as much energy as a 100-person dormitory to operate. While this is not a small amount of energy, when you consider a campus of about 25000 students, the energy consumption due to this room-sized computer is barely noticeable. For an individual operation it took around 100 J on this system. It is hard to make a comparison to what this energy actually means. There are 11 calories in a peanut, 4,184 J in a calorie, therefore a perfectly converted peanut gives you about 460 operations. For every joule of energy spent, ENIAC took 5 J of energy just to stay on. So it might be said that ENIAC may have been ever so slightly more efcient than the people operating it. But ENIAC was faster than the people operating it. It could execute around 300 multiplication operations per second and so it was very useful.

Solid state electronics came into full force with the introduction of a practical integrated circuit process by Robert Noyce in 1958.2 This led to a realization that was documented by Gordon Moore in 1965. Moore's Law famously states that the complexity of integrated circuits increases by a factor of two every two years. The semiconduction industry was advanced by the invention of the Metal Oxide Semiconductor (MOS) Transistor in 19633 and the road to perfection of the initial process for metal oxide transistors was initiated by Andrew Grove in 1964. Since then, the creation of a well-established set of business and engineering rules has continued to push the industry forward.4 In 1970, Intel, which was founded by Noyce, Moore and Grove, reported a revenue of just over \$4.2 million versus expenses of around \$5.6 million.5 The minimum printed transistor dimension was over ten microns (ten one millionths

2 Robert N. Noyce, 'Semiconductor device-and-lead structure', U.S. Patent No. 2,981,877 (April 25, 1961).

3 Kahng Dawon, 'Electric feld controlled semiconductor device', U.S. Patent No. 3,102,230 (August 27, 1963).

4 Tim Jackson, *Inside Intel: Andrew Grove and the Rise of the World's Most Powerful Chip Company* (New York: Viking Penguin, 1997).

5 Intel, *1970 Intel Financial Statement* (Arthur Young & Company, 1971), https://www. intel.com/content/dam/www/public/us/en/documents/corporate-information/ history-1970-fnancial-statement.pdf.

of a meter). In 2018, Intel's revenue was approximately \$70 billion with a minimum printed dimension approaching ten nanometers (ten one billionths of a meter).6 In 1974, another set of rules was established for the governing and scaling of the MOS transistor by Robert Dennard.7 When the complementary process was introduced in production around 1980, the minimum printed dimension of the transistor was reduced by a factor of 40% every two years (area reduction by a factor of 2). This became a well-documented and executable realization of Moore's Law. These governing economic and engineering principals, which allowed for continued functionality enhancements from device scaling held until at least 2015. What was also true during this time was that the energy per computation reduced at the same rate as minimum dimension. This has a direct analogy to the abacus example that was discussed previously. Smaller devices consume less energy. This minimum amount of energy per 'bead' movement can be found simply by recognizing that there are about thirty electrons moving through one volt of electric potential in the modern device. The minimum energy for this single bit change is then around 5\*10-18 joules, or 5 aJ. When the bit changes it must remove thirty electrons in addition to adding thirty holes (electron voids which behave as particles in semiconductors) or vice versa. The total energy under this scenario is 10 aJ. This is a good estimation of the capabilities of CMOS technology for a single bit. The present complementary MOS (CMOS) technology has not improved upon this minimum energy signifcantly since about 2011. In other words, the leading-edge energy efciency has not really improved considerably during that time. In truth, by lowering the voltage swing and by reducing the total number of carriers per bit, there is still room to improve and billions of dollars are spent every year by multiple governments and private companies to improve that fgure. However, these eforts have not yielded a signifcant amount of diference in the minimum amount of energy required to create and destroy a single bit that can meet the stringent requirements necessary to begin plans for

6 NASDAQ, *INTC Company Financials* (2019), https://www.nasdaq.com/ market-activity/stocks/intc/fnancials

7 Robert H. Dennard et al., 'Design of ion-implanted MOSFET's with very small physical dimensions', *IEEE Journal of Solid-State Circuits*, 9.5 (1974), 256–268. DOI: 10.1109/JSSC.1974.1050511.

production. When it is discussed that Moore's Law may be coming to an end, it is this fact that dominates the conversation.8

Fig. 1 From the author's (Barlage) notebook, c. 2000. A stock photo of a strand of DNA, adjusted to be at the same scale as a prototypical 15nm gate length MOS transistor produced by his group at Intel in 2000. This prototype transistor from dimensions are actually slightly larger than production transistors in 2019. From an atomistic view point, there is little room for further scaling.

What does the size 10 nm mean? Around the year 2000, prototypes of this size of device were made. To explain what this meant, the prototype device was compared to a strand of DNA. In Figure 1, a transmission electron microscope of a DNA strand is compared to the same scale as a prototype 10 nm CMOS device. The DNA strand is on the same scale. In fact, within the prototype device, there is an oxide flm with a critical dimension less than 1 nm in thickness. This is truly approaching the limits that scaling can achieve as 1 nm corresponds to a single molecular thickness of the material silicon dioxide, a key building component in this device. This is informative because typical production devices today are roughly at the same scale. There is simply not much room to become smaller. Our expected increase in efciency may truly be at an end, or at least within sight of the end.

8 Jonathan Koomey and Samuel Nafziger, 'Moore's Law might be slowing down but not energy efciency', *IEEE spectrum* 52.4 (2015), p. 35.

While the CMOS technology is physically limited to a minimum amount of energy as described above, a far more fundamental limit— Landauer's Limit—is determined by the second law of thermodynamics as it pertains to information. The minimum energy required to erase a classical bit is given by *kBT · ln*(2) ≈ 0.003aJ at room temperature. There is room to move towards this fundamental limit by at least a factor of one thousand, even more if the system is cooled, if an ideal switch can be found. But at present there is no device that has a lower energy per switch than the CMOS device. An examination of available competing technologies that rely on charge transport shows that at best, the minimum energy of bit fipping could be reduced to 1 aJ (reducing the number of electrons/holes to ten to make bit and reducing the supply voltage to 0.3 V, regardless of how the transistor is made, is about the best that can be achieved) and this too would be limited by the statistical nature of quantum mechanics at room temperature. So, the Landauer Limit remains fairly far away from being functionally realized.

In its most idealized realization of computational efciency, we see the supercomputer. The supercomputer is actually extremely efcient and makes use of the approximately 10 MW of power that it consumes for computation more efciently than many other computing devices. An examination of the current champion of supercomputers,9 Summit, located at the US Department of Energy Oak Ridge Laboratory (ORNL) near Knoxville, Tennessee, fnds that it can execute 200 Thousand Tera FLOPs or 0.2 ExaFLOPS every second.10 A quick calculation that looks at the power consumption and the rate of calculation:

$$\frac{Energy}{Floop} = \frac{Power \left(\frac{Joule}{second}\right)}{Compulation\ Rate \left(\frac{FLOPS}{second}\right)} = \frac{10MW}{0.2 \cdot 10^{18} \left(\frac{FLOP}{second}\right)} = \frac{50pf}{FLOP}$$

indicates that every FLOP consumes 50 pJ or 50\*10-12 J. This is 10,000 times more than the minimum energy per bit required in state art microprocessors and about 10,000,000 times more than the minimum

9 'US Debuts world's fastest supercomputer' (n.a.), *BBC News* (June 11, 2019), https://www.bbc.com/news/technology-44439515.

10 Jonathan Hines, 'Stepping up to summit', *Computing in Science & Engineering*, 20.2 (2018), 78–82. DOI: 10.1109/MCSE.2018.021651341.

energy per bit defned by the Landauer Limit. In truth, each operation requires many individual bit transfers to perform one calculation. A further investigation shows that this disparity can be explained by the weakest link in energy efciency. This is when the processing unit needs to access memory. This is the cause for hope in terms of engineering. While the reduction in energy and size in producing devices to access information is reaching an end there are simply a lot of inefciencies to reduce to make the overall system more efcient.

10 MW, the power required to drive the world's more powerful supercomputer, is a lot—nearly enough power to meet the energy demand for 10,000 homes. While large, this amount of power available is not nearly as large as the amount of power required to supply a typical server farm that is currently being operated by companies such as Google or Facebook. Just 200 km or so east of the Summit supercomputer is a Facebook server farm. The power consumption of this operation is not published. The majority of the people that operate this server farm are in locations that are far away from this modest looking Forest City, North Carolina information hub. The physical size of this facility is about ten times the size of the Summit facility. The density of microprocessors inside the facility is about the same. If an extrapolation is made, you could note that the Facebook facility consumes about ten times the amount of power as the Summit supercomputer. That would imply that the energy consumed by this server farm would be equivalent to 100 MW. This is enough power to supply 100,000 homes. Both Google and Facebook, have committed to moving to renewable energy to help manage the high energy bills that they face from their data centers. Putting it into perspective, it takes over 70,000,000,000 kWh per year to run all data centers in the U.S.,11 and the average household takes about 10,000 kWh per year.12 It should be noted that the decisions are predominately driven by proft motive and not an altruistic desire to save the earth. Renewable energy has become more cost efective than non-renewable sources and these

11 Arman Shehabi et al., *United States Data Center Energy Usage Report* (Berkeley: Lawrence Berkeley National Laboratory, 2016), https://www.osti.gov/servlets/ purl/1372902/.

12 US Energy Information Administration (EIA), *How Much Electricity Does an American Home Use?* (Washington: 2019), https://www.eia.gov/tools/faqs/faq. php?id=97&t=3.

technology companies are taking advantage of this. In April of 2018, Google announced that it had achieved 100% use of renewable energy in its operations.13

### The Rising Demand of Computation

The total consumption of energy use for computation is on the rise and this rate of increase shows no signs of slowing down. Depending on the source, the percentage of energy consumption dedicated to computation has risen from under 2% in 2000 to over 6% in 2012 and the fgure continues to rise.14 While the exact fgure is currently unavailable, recent trends would suggest that today at least 10% of all electrical power produced in the world is dedicated to computation. It is estimated by 2025, this fgure could rise to as much as 20% of total worldwide power generation.15 It should be noted that this fgure includes the energy that is used for displays and that in 2000, that made up a large percentage of the resources required for computation. Today, that human interface is a mere fraction of the total energy expenditure required in calculation, most of the energy that is consumed as you are watching the most recent viral cat video is consumed in a server farm far away from you, not from the screen directly in front of you. To put this in perspective, in the year 2000, worldwide energy consumption for computation was still a minor consideration for overall energy consumption in the world. Refrigerators were a bigger concern than computers in 2000.

2000 is a good year to choose as the time when we transitioned from using discrete computers as our primary resource for computation to when we use networked resources to create our primary value. This dramatically pressured the demand for computation. Metcalfe's Law states the more similar devices that are connected on a network, the greater the value of the collective network by a factor of N2 (it has been recently suggested to modify this expression to N\*ln(N) instead of

13 Urs Hozle, '100% renewable is just the beginning', *Google* (5 April, 2018), https:// sustainability.google/projects/announcement-100/.

14 United States, US Energy Information Administration (EIA), *2012 Energy Consumption Survey* (Washington: Government Publications, 2017), https://www. eia.gov/consumption/commercial/reports/2012/energyusage/.

15 Shehabi, et al. (2016).

N2 ).16 When this happened value increased dramatically for companies that made use of networked machines to add value. Among the best example to illustrate the increasing value of networked computers is the Amazon market cap stock evaluation. Amazon is the leader in server farms for hire. Since 2005, Amazon, a company that depends dramatically on the number of interconnected machines, has increased its value from \$14 billion to nearly a trillion dollars. In that same time period, Intel the primary maker of computation machines rose from a value of \$100 billion to \$200 billion. More machines, N (Intel's value driver), were attached to the Internet and the value of a server farm was recognized by businesses, universities and governments worldwide and increased as N2 (Amazon's value driver).

The energy consumption required for computation has increased at a steady but large rate but less than the amount of operations that are being performed. Furthermore—on demand video and social networking became increasingly dominant and the energy consumption of computation started to become a more relevant number. These two applications drove the increasing demand for bits as the energy cost per bit became increasingly lower. In the year 2000, the power supplies driving the server farms were barely 50% efcient and today that number stands closer to 90% and yet the total power consumption continues to grow. Computation, and its supporting infrastructure continued to be more efcient and this increase in efciency was outpaced by the demand for more bits. An irony of technology development, known as Jevon's Paradox, says that as you make more efcient use of a resource, society as a whole will not use less of that resource, it will actually use more.17 When things get more efcient more people want those things. We lived in a world where advanced computation was found only in the domains of businesses, governments and the scientists who needed advanced computation. Jevon's Paradox of consumption started to prevail as computation became more efcient. Computation spread to the masses and the energy consumption rose dramatically. The number of operations

16 Carl Shapiro and Hal R. Varian, *Information Rules: A Strategic Guide to the Network Economy* (Boston: Harvard Business Press, 1998).

17 William Stanley Jevons, *The Coal Question* (New York; The Macmillan Company, 1906).

used has been increasing at an extremely high rate while the energy per operation decreases at an almost equally fast rate through 2015. The combination of these factors is what drives the demand side of computation. It was the continued drive of reduced transistor size that kept the energy demand in check for much of this time, however, as discussed previously, the gains that come from transistor scaling are no longer available with CMOS scaling alone. It can be expected that in the near future, the energy expended per bit at the leading technology will be restricted to no less than 10 aJ. Working towards the 1 aJ limit and the Landauer Limit will be an unlikely path toward greater energy efciency, despite the fact that this was the driver from more than ffty years.

This insatiable demand for bits of information is captured in Figure 2. This approach to the representation of this type of data was originally presented in an advocacy report from the Semiconductor Research Corporation and the Semiconductor Industry Association18 and is updated here with a further breakdown of the entire world energy supply. Here, we can see the rising demand for information that is literally catching up with the total capacity for world energy production. By estimating the total number of bits that are used and monitoring its rise we can start to grasp the potential problems at hand. In 2010, it was estimated that for every bit of information that was processed it took 10 fJ to process. This is in fact about 2000 times the minimum amount of energy it should take today. This extra amount of energy comes from the fact that when a bit is processed it must also access memory and move this bit from one part of the computer system to another. The number of bits processed in the world increased by a factor of approximately 1000 from the year 2000 to the year 2010. From 2010 to 2015, this trend continued and there is little likelihood that this trend will slow down. Fortunately, as the world's demand for data increased, the efciency of the way in which data was processed also increased. The minimum energy per bit reduced by a factor of about 100 during that time. Thus, the total energy still increased. The world went from having almost no noticeable energy consumption due to

18 Semiconductor Industry Association, *Rebooting the IT Revolution: A Call to Action* (2015), https://www.semiconductors.org/resources/rebooting-the-it-revolution-acall-to-action-2.

computation, <2% of all energy produced on the grid being consumed in computation, to nearly 5% in 2010. The only reason that the impact on energy consumption has not been higher has been the steady gains throughout the electronic ecosystem that Moore's Law has given us for over ffty years.

Fig. 2 Computational energy used with respect to total number of bits that are being demanded and the increasing amount of worldwide energy supply. This data is adapted from a joint report from Semiconductor International Association and the Semiconductor Research Corporation (2015).

Table 1 Comparison of fundamental units of energy consumption for computation.

A comparison of the required energy consumption indicates paths to strategies to reduce the total system energy in Table 1. The diferences in the total energy consumed yields pathways that can be followed in meaningful engineering strategies to meet the expected 1000X/decade increase in demand for computation. Reducing the number of bits per operation is the most notable path to meaningful gains in energy

19 Chibuzor Aguwa, 'Top 5 fastest supercomputers in the world 2018', *Blogginfotech* (July 10, 2018), https://blogginfotech.com/top-5-fastest-supercomputers-in-theworld-2018/.

20 Semiconductor Industry Association, *2011 International Technology Roadmap for Semiconductors (ITRS)* (2015), https://www.semiconductors.org/resources/2011 international-technology-roadmap-for-semiconductors-itrs/.

21 Jin Cai (organizer), 'Scaling survival guide in the more than Moore Era', International Electron Devices Meeting (IEDM) 2018, San Francisco, 2 December 2018, short course.

22 Rolf Landauer, 'Irreversibility and heat generation in the computing process', *IBM Journal of Research and Development*, 5.3 (1961), 183–191, https://doi.org/10.1147/ rd.53.0183.

efciency. In fact, great strides have been made in this area. The most obvious example of this can be found within the supercomputers built at ORNL. Reducing the complexity of operation, i.e. reducing the number of bits required to perform an operation, allowed the energy per bit to be reduced by a factor of three from Summit, commissioned in 2018 to Titan, which was frst turned on in 2012. The more impressive gains have come from the server world, where introductions to approximate computing has led Google to be able to perform searches with 200 times less energy with the same technology than they were able to use just a few years ago. To achieve these ends, Google uses what is commonly referred to as accelerators to perform specifc computations and to streamline the computational efciency per operation. They changed the amount of energy by changing the foating point energy computational energy required. They moved from 50 pJ to 400 fJ (125 times less) without changing the underlying technology. Reducing access to memory is also a means of improving computational efciency. If the memory does not have to be accessed, each act of bit destruction could be reduced to the technology minimum of 10 aJ. This is a factor of 1000 to be gained by incorporating memory directly into the central processing unit. This gain could be achieved without further altering the core transistor technology. Changing the core technology to an ultimate limit that still uses principles of well-established semiconductor physics, could allow the number of electrons and holes to be reduced by a factor of 10 and the voltage that the system is operated to be reduced by a factor of 2. The maximum possible gain that could be obtained would be a factor of 20. This pales in comparison to the amount that stands to be gained from more efcient architectures and designs. The last is that driving towards the Landauer Limit and the gains that can be found from improving the underlying semiconductor technology in some capacity. Can there be a single electron device that can operate without error and be manufactured and considered reliable? That answer is not certain and will certainly be pursued in the coming years by the organizations that have the most to gain from the increased gain in efciency.

The architecture gains that were alluded to in the last paragraph form the foundation for the applications that will drive the new sources of demand in the coming ten to twenty years. What was loosely recognized as the smarter way to do computing, and is being implemented, is the foundation for what could loosely be referred to as neuromorphic computing. This type of computing is, in some sense, the hardware realization of artifcial intelligence. We have become familiar with artifcial intelligence in our daily lives through many applications that exist just below our awareness. These applications have been almost universally software driven. This next step, incorporating these algorithms directly into the hardware has already begun to yield fruits in efciency and stands ready to be the next driving force in computer evolution. This is where Jevon's Paradox will again take hold as the next wave of computation will drive artifcial applications such as self-driving vehicles. While watching the latest viral cat video took a measurable amount of energy, being driven to work by an algorithm operating on a remote computer connected by the 5G network will take 100–1000 times more energy. Yet that algorithm when run today will be executed in a way that is 1000 times more efcient than if it were to be executed with technology available in the year 2000. Autonomous vehicles are just one of the many artifcial intelligence applications that are becoming more prevalent and constitute just one more increase to the energy demands of computation.

Pursuing neuromorphic computing is a realizable approach to computing improvements, just like CMOS scaling was the most realizable approach to gains in efciency from 1980 through 2015. An important fact that should be noted when considering advanced CMOS processes: in 2000, there were nearly thirty companies in the world that could produce the most advanced silicon devices. Today there are only three. In 2000, the typical minimum dimension in production at the advanced node was 180 nm and today it is 10 nm. The capital investment for every 40% shrink has increased even faster than 40% and slowly eliminated the number of companies able to keep up at the leading edge. In terms of energy consumption per bit, that represents a nearly 400 times improvement from the year 2000 to today. Of these three companies, one is what is referred to as a foundry service (Taiwan Semiconductor Manufacturing Company, TSMC), one ofers both internal and external services (Samsung) and the last (Intel) only manufactures products that are issued under its brand. Foundry services enable companies to produce chips with their own designs with the most advanced silicon. Recently former participants in this economic sector, IBM and Global Foundries have bowed out of the race at the edge as neither could compete due to fnancial considerations.

Today, Apple and Huawei do not produce silicon microprocessors; but they do build the most advanced cell phone microprocessors in the world in the same multi-billion-dollar facility in Taiwan.23 The most advanced products and computationally energy efcient devices in the world are built side-by-side, using the exact same technology. What does this mean for experimental research in neuromorphic computing at the leading edge? Both advanced microprocessors and neuromorphic computing require an extraordinary amount of capital; this largely prohibits universities and other smaller facilities from being able to do experimental research in this area. What can be said about computing is that, ultimately, the most energy efcient endeavors have prevailed in the marketplace and slowly eliminated competition. Neuromorphic computing, and its related approaches, shows a high potential for energy gains and for increasing its chances of a successful and signifcant market penetration. These gains, however, only occur when operating within the smallest node. Only the largest players who have access to the smallest nodes will be able to compete in the feld. The best ideas that enable energy savings will largely not make it out of the laboratory without access to opportunities for experimenting within these advanced nodes.

#### Navigating the Future of Computing

Unlike the last ffty years, where the gains came largely from material, device design and technology implementation, the gains of tomorrow will come from improved architectures. This approach is commonly referred to as accelerators or heterogeneous computing. Accelerators are application specifc integrated circuits (ASICs) that ofer advantages in speed or energy efciency for very specifc tasks. The number of people that can be trained in these new architectures will be limited by the companies that control the leading edge of technology. In the past, access

23 Malcolm Owen, 'Apple, Huawei both using 7nm TSMC processors, beating out Qualcomm and Samsung', *Apple Insider* (October 2, 2018), https://appleinsider. com/articles/18/10/02/apple-huawei-both-using-7nm-tsmc-processors-beatingout-qualcomm-and-intel.

to advanced device manufacturing could be realized with relatively small investments. Today, at least in the near future, that capability is lost. While companies such as Apple, Amazon or Google can aford to hire either Samsung or TSMC to build speciality chips, the rest of the world cannot. This limits the open expression of ideas that has largely defned the semiconductor and digital applications world for most of its existence, and has driven the innovation cycle. The expense of access to a resource which has largely become a commodity, as necessary to modern life as petroleum, will be held in the hands of fewer and fewer organizations as time progresses. Recognition at the government level is what is required to ensure this openness, and to ensure that this everincreasing commodity is not unnaturally limited.

National strategies to maintain and create access in this area are being pursued by both China and Saudi Arabia. Both nations have committed an extraordinary amount of resources to enhance the sovereignty of the data that fows through both nations. India has also announced plans to build its own national foundry to rival that of TSMC. These are examples of governments that recognize that computation is fundamental to their growth and survival as a nation. China has committed to invest \$150 billion USD through an investment fund (Tsinghua Unigroup) that is matched by another \$300 billion in corporate investment to develop competitive manufacturing at the most advanced nodes of semiconductor manufacturing. In Saudi Arabia, there is an investment fund, nearly the size of China's investment fund, to establish semiconductor manufacturing in the region. The United Arab Emirates privately owns Global Foundries, the third largest semiconductor foundry in the world. The United States has more recently recognized the attention that is required by initiating legislative discussion of this with the Semiconductor Foundry Act of 2020.24

Our future world of computing will look a lot like the world that we live in today, however the biggest change will be what drives that world behind the scenes. There will be an increasing number of specialty chips

24 Shannon Davis, 'American Foundries Act would provide needed investments in U.S. semiconductor manufacturing, Research', *Semiconductor Digest* (June 26, 2020), https://www.semiconductor-digest.com/2020/06/26/american-foundries-actwould-provide-needed-investments-in-u-s-semiconductor-manufacturingresearch/.

to perform specifc tasks within the computing environment. We already see the frst steps in phones that ofer facial recognition, delivered by an additional chip that is designed to specifcally address facial recognition algorithm—not general computing. The energy that would be required in a general-purpose chip would be too high to maintain, so an accelerator designed to do nothing but provide facial recognition enables energy efciency for this task. This is a good example of the many accelerators that are likely to come. The energy efciency that is gained is obvious. Future tasks will see a master controller central processor with many accelerators providing functions of which we have not yet fully conceived. The nations, companies and individuals that can ofer advancements in these areas will lead the semiconductor industry and subsequently the information technology of the future.

#### Acknowledgement

The authors acknowledge support of the work through a grant from the University of California, Lawrence Berkeley National Laboratories and the U.S. Department of Energy. For directed nanoscale research in Computational Electronics. Subcontract No. 7334354.

#### Bibliography

american-foundries-act-would-provide-needed-investments-in-u-ssemiconductor-manufacturing-research/

## 6. Sustainable DNA: InConversation

*Mél Hogan and Deb Verhoeven*

Big Tech supports social media, the stock market, insurance companies, scientifc research, fnancial transactions, mass surveillance and monitoring, the 'Internet of things', 'smart city' sensors and grids, and mobile communications for Internet users writ large. By most industry accounts, data centres—and the cloud infrastructure that undergirds it—has become the most important sociotechnical system of our time, but also the least sustainable. Interestingly, one of the alternatives to these water- and energyintensive data storage solutions has emerged from advancements in synthetic DNA technologies, now touted by the industry as a safer, greener and more efcient storage medium. But how did we get here? How might ideas of 'sustainability' and 'efciency' function in this context? In conversation, Mél Hogan and Deb Verhoeven discuss the idea of 'Sustainable DNA'—in its various instantiations as an object of critical media studies.

By most industry accounts, data centres and the cloud infrastructure that undergirds them have become the most important sociotechnical system of our time, but also the least sustainable. During these pandemic times, for example, with many on lockdown, self-isolating or under quarantine, we have seen a surge in Internet demands: up by 70% for work, research and entertainment.1 Scientists worldwide are busy

1 Mark Beech, 'COVID-19 pushes up Internet use 70% and streaming more than 12%, frst fgures reveal', *Forbes* (March 25, 2020), https://www.forbes.com/sites/ markbeech/2020/03/25/covid-19-pushes-up-internet-use-70-streaming-morethan-12-frst-fgures-reveal/#6d22fd223104.

sequencing the genome (DNA) of COVID-19 while others sit at home creating and streaming more media content than ever. There's probably been no time like this current pandemic for thinking through the ways in which something like DNA and the Internet coexist (if not collide) as constitutive paradigms. Specifcally—and what we discuss here—one of the alternatives to water- and energy-intensive cloud infrastructures has emerged from advancements in synthetic DNA technologies, now hailed by the industry as a safer, greener and more efcient medium for data storage. But how did we get here; from large scale cloud storage to the molecular? How might ideas of 'sustainability' and 'efciency' function in this context? In conversation, Mél Hogan and Deb Verhoeven discuss the idea of 'Sustainable DNA storage'—in its various instantiations as an object of media and critical infrastructure studies. The following stems from a talk given by Mél Hogan, delivered at the University of Alberta's 2019 Sustainability Lectures.

**MH:** I frst came to think of 'sustainable DNA' storage by positioning it as a response to data centres. The current infrastructure for data storage generally includes tape and hard drives in large scale data centres, or what we've come to simply think of as 'the cloud'. As a media object, DNA data storage is a great object for analysis, not only for the way it ofers continuity, in an analysis of storage technologies, but also for the way it makes us confront code anew. What makes DNA such a viable storage modality is, in part, how compatible the code of DNA—its four constituent chemicals: cytosine (C), thymine (T), guanine (G), and adenine (A)—is with the binary code—the zeros and ones of digital data. So, to explain it most simply, the process of converting data into DNA is one of translation and conversion into various combinations of the four possible acids: C, T, G, or A. The use of the DNA sequence as a storage medium is expected to take of within the next ten years, with both universities and commercial companies leading the way.

\*\*\*

**DV:** My refections on sustainable DNA developed out of my experience building digital archive infrastructure and then a serendipitous encounter with a taphonomist. This got me thinking: if DNA is used to store information, what would be the nature of its decay at the informational level? How sustainable is 'sustainable DNA' in both practical and philosophical terms?2

Media Studies encourages us to approach the question of sustainability in terms of discursive histories including complex narratives about origins, provenance, inheritances, temporality, longevity and 'the archive'. In thinking specifcally about the industrialisation of DNA (its storage and its use as storage media) these approaches interleave with many issues brought into consideration by Critical Infrastructure Studies.3 Infrastructure establishes the conditions for the possibility of connection. DNA can be thought of in a sense as a form of infrastructure. And the 'connectivity' it enables is existentially defning. We hear this when people talk about 'the DNA' of an organisation or a system meaning its constitutive, connective tissue. DNA here is 'foundational' in the sense that it is presented as conceptually irreducible (i.e., as essential) and in the way it cuts through time by producing a kind of 'present-day provenance' in the form of an authoritative ontological explanation (i.e., as essentialist). This is the kind of 'originary thinking' that I've written about previously in my work on sheep as foundational tropes. Originary thinking is a totalising thinking that purports to account for everything by getting to the ground of things.4 Forget about Dolly the cloned sheep. Even woollen apparel now comes with DNA certifcation (see Figures 1 and 2).5

This understanding of DNA as 'connective' gives rise to specifc narratives about how our DNA links us—I'm thinking of the widely quoted statistic that we share 99.9% of our DNA with the person sitting next to us for example. But these claims of a defning human capacity for sharedness also touch aspects of the world that are typically excluded from western human taxonomies and that are now drawn into

2 Deb Verhoeven, Mike Jones and Stephen Loo, 'Replicating the replicants; or, do archivists dream of downloadable sheep?', paper delivered at 'Australian Society of Archivists—Archives in a Blade Runner Age: Identity & Memory, Evidence & Accountability' (Perth, Australia, September 25–28, 2018).

3 On Critical Infrastructure Studies, see https://cistudies.org/, which includes a detailed bibliography outlining this emerging feld of interest. See also Jean-Christophe Plantin, Carl Lagoze, Paul N Edwards and Christian Sandvig, 'Infrastructure studies meet platform studies in the age of Google and Facebook', *New Media & Society*, 20.1 (2018), 293–310, https://doi.org/10.1177/1461444816661553.

4 Deb Verhoeven, *Sheep and the Australian Cinema* (MUP: Melbourne, 2006), pp. 15–19.

5 Australian Wool Network, http://woolnetwork.com.au/dna.html.

a connection with us and that therefore redefne human relationality as something that includes the 'more than human'. My favourite is the line about how more than half of our genetic code is the same as a banana. What does this even mean? I guess for me it prompts us to think much more broadly about the co-dependencies or relationality at the heart of

Figs 1 (left) and 2 The use of DNA to ensure the 'naturalness' of garments through provenance tracing. Photographs by Deb Verhoeven (2020).

our 'lifeworld'.6 It evokes for me what Indigenous theorist Shawn Wilson says in a diferent context: 'Rather than viewing ourselves as being *in* relationship with other people or things, we *are* the relationships that we hold and are part of',7 or when Kim TallBear writes about understanding existence as a matter of 'being in good relation' rather than as the assertion of a defned point in hierarchical taxonomies.8

To take this to a more prosaic level. We might also want to understand the opaque interconnections in the digital infrastructure that have arisen around the sale of DNA related services—I am thinking here of Ancestry.com and 23andMe, for example. The familial relationships

6 Maggie Walter, 'The voice of Indigenous data beyond the markers of disadvantage', in *Grifth Review 60: First Things First*, ed. by Julianne Schultz and Sandra Phillips (Brisbane: Grifth University, 2018), pp. 256–263.

7 Shawn Wilson, *Research is Ceremony: Indigenous Research Methods* (Halifax, Nova Scotia: Fernwood Publishing, 2008), p. 80.

8 Kim TallBear, 'Caretaking relations, not American dreaming', *Kalfou: A Journal of Comparative and Relational Ethnic Studies*, 6.1 (2019) https://doi-org.login.ezproxy. library.ualberta.ca/10.15367/kf.v6i1.228.

that underpin these companies aren't obvious but are structuring. Anne Wojcicki, co-founder of 23andMe was married to Sergey Brin, co-founder of Google and investor in both 23andMe and Calico, a multibillion-dollar 'seed' funding company whose express mission is to understand and infuence the genetic basis of aging, and which is Ancestry.com's main research partner. I'm not even going to debate whether a company like Calico is much more than a vanity project for Silicon Valley millionaires with an extended life wish. But I do want to point out that it isn't just our DNA that we share in so many ways. It's also the DNA data itself that is being shared unexpectedly—between a range of personally interrelated companies. I am tempted to think of them as 'geneo-platforms' or even 'geno-platforms': digital DNA platforms that are themselves linked genealogically.

It is not therefore without untold levels of irony that platforms devoted to the procurement of vast amounts of DNA data participate in a geneo-fnancial network of digital infrastructure that is fundamentally contributing to the very problem it seeks to solve—human longevity. This is the key point I feel that Mél you've been making in your extended research on DNA infrastructure and its environmental impact.

**MH:** In the talk I gave at the University of Alberta on the idea of 'Sustainable DNA',9 I was making a link between Big Tech's investments in genomics and how that gave rise to both the idea and material support of using synthetic DNA to store media. I've since visited the lab at the University of Washington where the frst fully automated synthetic DNA data storage machine10 was invented, funded (mainly, I think) by Microsoft. The idea is that synthetic DNA—which is essentially four chemicals—is a denser and less energy-intensive medium for data storage. And in my talk, I was demonstrating that the mechanics came easily enough, but the legacy of genomics is also imbricated in the logics of these technologies… scientism, religion, settler-colonial science, eugenics etc. We need to talk about these things in order to talk about sustainability when it pertains to DNA data storage, too. And, like you

9 Mél Hogan, 'Genomic media/sustainable DNA', paper delivered at the 'University of Alberta: Sustainability Lectures' (University of Alberta, September 13, 2019).

10 Jennifer Langston, 'With a "hello," Microsoft and UW demonstrate frst fully automated DNA data storage', *Microsoft* (March 21, 2019), https://news.microsoft. com/innovation-stories/hello-data-dna-storage/.

say (along with Kim TallBear and Jess Kolopenuk among others), in terms of relations; to other species, to land, to planet, to universe.

The recent turn to the misguided belief that DNA data storage is a 'solution' sees this 'solution' as curtailing the environmental impacts of current storage modalities, such as wasteful products and processes, and demands on rare earth minerals, water and electricity. Considering that the millions of data centres around the world currently account for 2% to 8% of global energy use to manage the 2.5+ quintillion bytes of data created each day, genomics data (i.e., collecting genomes of humans, plants, animals and viruses, etc.) actually intensifes environmental problems in terms of generating huge amounts of data that then needs to be processed and stored… and, in the context of global warming (and now, the pandemic!), this data requires vast infrastructural transformations. When I last visited a data centre in Stockholm, the CEO of Bahnhof told us—as he was showing of the highly energy-efcient server room—that: 'a few Google searches use the same amount of energy as boiling an egg'. I use the image of an egg often in my talks so that it sticks with the audience, but I also make the point that personal or individual consumption isn't the crux of the problem, generally, when it comes to environmental questions.

**DV:** I love this idea of boiling eggs as a measure of digital infrastructure efciency. As a metaphor it is a kind of genius, eggs being a DNA repository or storage facility. And of course, we already talk about 'easter eggs' and 'server farms' in the vocabulary of computing. But what if Google searches really were eggs? Maybe then we would be more inclined to ask if they're the product of caged or free-range data farming? We might at diferent times prefer our results scrambled rather than carefully presented sunny-side up? And maybe when the results rise quickly and threaten to collapse with unfulflled expectation then we would recognise them as unstable 'soufe searches' rather than the 'hard boiled' option we are told we must swallow?

**MH:** Metaphors do function to reveal meanings by association but, as we know from the idea of 'cloud computing'—metaphors can also obscure and misrepresent. Sometimes, in fact, this is the objective.

**DV:** Yes. When you spoke at the University of Alberta, I had the image of a self-entwined helix running through my head the entire time, especially in terms of the way you interleaved both the style and the substance of your commentary on data centres and DNA (specifcally the entanglement between DNA and digital code).

Metaphor itself is a kind of connective tissue, bringing into frame things that seem at one level to be dissimilar but that once compared bear on each other in expressive ways. Derrida says something along the lines that metaphor is never innocent,11 suggesting that it limits meaning because it reiterates a binary or at best a fxed relation like the triples of a graph: subject, object, predicate.

I guess, for me, if we rethink relationality to be a capacity rather than the description of a connection then we might arrive at a diferent way of thinking about metaphor, too. How could the process of metaphoric connection support complex, overlapping, co-constituting, non-binary understandings of infrastructure? So, sure, clouds give rise to the idea that computing infrastructure is somehow 'natural' and out of the reach of ordinary human capability; that computing infrastructure is just a way of recuperating foods of data (in the form of vapour). Clouds are an obvious continuation of the diluvian descriptions of data inundation. And the term swiftly sidesteps the terrible way in which data centres, with their profigate use of water resources, mock these metaphors.

But clouds are also unstable and dynamic processes of conversion. They accumulate only to burst, they constantly threaten their own dissipation. They are neither 'infra' (existing below) nor 'structure' in any meaningful sense. Instead they conjure a collapse of infrastructure and perhaps therefore metaphor itself. So, the poverty of cloud imagery as an analogue for computing infrastructure isn't the real problem; the real problem is the fragility of infrastructure (and therefore also metaphor and perhaps representation itself). We might take this opportunity to think about how both metaphor and infrastructure can propose an inclusion that isn't necessarily coercive.

**MH:** Genomics research has embedded within it a computational rationale that is constitutive of thinking of DNA as *code*, and as *coded*. Genome scientists worldwide estimate acquiring, storing, distributing and analyzing approximately 2 billion human genomes, mapped

11 Jacques Derrida, *Writing and Diference* (Chicago: The University of Chicago Press, 1980).

globally by 2025, amounting to 40 exabytes of data. With genomics—as with many big data projects—the machine is essentially fed the largest amount of data to generate patterns from which fndings are then made using artifcial intelligence and other algorithms. But the key (unanswered) question is what constitutes 'genomics' exactly? Nobody I've asked seems to fully know because it's an ever-evolving answer. There have, however, been many scholars working to dismantle the conceptual, cultural and material centrality of the gene by addressing in some capacity the role of code and technologies for rendering visual and mappable genetic information.

In brief, here are some important takeaways by the key thinkers:

*Lily E. Kay* writes: 'The products of science and technology are sociotechnical; they work because they are embedded not only in material practices but also in cultural practices that stabilize and naturalize the technologies for producing knowledge and power. In the case of the genetic code, it is biopower'.12

*Evelyn Fox Keller* explains that in the early stages of the Human Genome Project there was a 'precocious simplicity' to the new wave of molecular genetics that explained what genes 'do' as encoding enzymes. Defects in genes mean defects in the enzymes which correlate to trait abnormalities. This was the simple formula of genetics that inspired the quest to linearly sequence DNA as code. As she explains: 'The answer was stunning in its simplicity; also, it had the elegance of a mathematical equation'.13 DNA makes RNA and RNA makes proteins and proteins make humans. Keller further argues that this formulation of DNA, as code, pushed scientists to read gene function by way of their structure. She writes: 'It established DNA as the molecule that not only holds the secrets of life but also executes its cryptic instructions—it was, in short, the Master Molecule'.14 But she casts doubt on the given agency of genes, or the idea that all genes work the same way, and invites us to consider other agents that call genes into action. Rather than act, genes are activated. While some genes encode RNA to proteins, as our simplifed idea of genetics imagines, some genes play other roles—as regulators,

12 Lily E. Kay, *The Molecular Vision of Life: Caltech, the Rockefeller Foundation, and the Rise of the New Biology* (Oxford: Oxford University Press, 1999), p. 19.

13 Evelyn Fox Keller, *The Century of the Gene* (Cambridge: Harvard University Press, 2002), p. 54.

promotors, terminators, leaders, activators. Keller asks, 'what then should we count as the beginning and end of a gene?'15

*Kaushik Sunder Rajan* explains that the frst phase of genomics was very much about 'the generation of databases'.16 These databases are the output of DNA (genetic) mapping and/or (genomic) sequencing. Science, he explains, functions within 'its own authority by virtue of its ability to generate scientifc "fact"'.17

*Jenny Reardon* echoes this statement twelve years later, writing that human genomics companies still face the problem of how 'to create valuable knowledge from genomic data' in the frst instance.18 Genomics do not contain inherent value, value is created from genomics.

As *Alondra Nelson* writes: 'the special status aforded to DNA as the fnal arbiter of truth of identity is vividly apparent in the language we use to describe it […] the language of DNA pervades our cultural imaginations''. She writes that 'hyperbolic phrases such as "code of codes," "the holy grail," "the blueprint," the human "instruction book," and "the secret of life" suggest a core assumption about the received omnipotence of genetics'.19

*Ashley Dawson* applies this insight over DNA as a code and suggests that, 'over the second half of the twentieth century, communication was transformed cybernetically into information, and information was subsequently reduced electronically to digital bytes. In tandem with this process, biological life was parsed as a molecular code in the form of DNA's strings of four basic nucleotides: cytosine (C), guanine (G), adenine (A), and thymine (T)'.20 Similarly to Sunder Rajan (and others), Dawson makes the case that biology (or, bios) can be circulated 'as information, as commodity, and as material artifact'.21

15 Ibid., p. 59.

16 Kaushik Sunder Rajan, *Biocapital: The Constitution of Postgenomic Life* (Durham: Duke University Press, 2006), p. 28.

17 Ibid., p. 19.

18 Jenny Reardon, *The Postgenomic Condition: Ethics, Justice, and Knowledge after the Genome* (Chicago: University of Chicago Press, 2017), p. 123.

19 Alondra Nelson, *The Social Life of DNA: Race, Reparations, and Reconciliation After the Genome* (Boston: Beacon Press, 2016), p. 4.

20 Dawson, Ashley, 'Biocapitalism and culture', paper delivered at 'Spring 2015 Colloquium Series' (UC Davis, March 4, 2015), http://environmentsandsocieties. ucdavis.edu/fles/2015/02/Dawson\_Biocapitalism-Culture.pdf, p. 6.

21 Ibid.

*Catherine Bliss* also identifes that the core problem of sociogenomics has to do with our central beliefs about what constitutes 'humanness' and issues a warning: 'DNA, with its portrayal of the truth of human beings, wins the day. So essentialism can thrive and amplify even as arguments are assailed against it'.22

*Jessica Kolopenuk*, 23 and *Reardon and TallBear*, 24 have put into question the ways in which DNA molecules have been harvested by the settler scientifc community as almost always separate from the body. This disconnect has meant a lack of attention paid to issues that pertain to ownership in an embodied sense.

There are others, of course… but together these give us a pretty substantial and ongoing critique of the gene in terms of its perceived and propagated unassailability by the genomics industrial complex!

**DV:** The trick here is not to let the powerful if elusive conceptualisation of DNA pass without also understanding the impact it has had, not just on the defnition of contemporary science, but also on the allocation of research infrastructure. The wholesale optimism generated by just the idea of DNA resulted in signifcant (fnancial and political) investment. And yet, so far the outcomes have not lived up to the hype associated with the launch of the Human Genome Project of 1990–2003, for instance.

I think there is an ethical question here around scientifc accountability. And one that has really haunted science from the outset. At what cost? Do the means (intellectual efciency and/or reproducibility, for example) justify the ends? And where human life is now concerned—the endings? Aspirations for efciency (Occam's Razor as one example) and scientifc reproducibility (validation as an agreement of results) drive the shape of digital research infrastructure around the world, although they are a highly linear and specifc mode for measuring the extent of knowledge. I believe we need to be asking, how has an institutional disposition devoted to scientifc accountability,

22 Catherine Bliss, *Social by Nature: The Promise and Peril of Sociogenomics* (Stanford: Stanford University Press, 2018), p. 113.

23 Jessica Kolopenuk, 'Miskâsowin: Indigenous science, technology, and society', *Genealogy*, 4.1 (2020), https://doi.org/10.3390/genealogy4010021.

24 Jenny Reardon and Kim TallBear, '"Your DNA is our history": Genomics, anthropology, and the construction of whiteness as property', *Current Anthropology*, 53.5 (2012), S233–S245, https://doi.org/10.1086/662629.

understood through acts of 'reproducibility', actually compromised a broader need for social 'reproduction' or sustainability?

**MH:** Because of this ongoing belief that more data means more knowledge, we are willing to invest in, build, program and maintain at all costs the machines that will, one day, reveal to us great truths…

I think that genomics thrives in this liminal space… a kind of relentless and just around the corner revelation is promised to us. 'We've invested so much, we can't stop now…'

In the meantime, genomic data is mined for all sorts of capitalist ventures, which may be an always temporary ideal state for the industry. It can live of the promise for a few more decades. What is happening in the meantime is that a new logic of code is becoming culturally entrenched. We need code because we imagine it to be the simplest trace of humanity we can leave behind, as our legacy, at the end of the world, because it is the simplest formulation—the most basic unit—of how we now understand ourselves scientifcally.

**DV:** Yes! And here is where it gets super interesting. The very code that we are willing ourselves to believe is the most essential defnition of our 'selves', the very code we are squandering untold natural resources to try and understand at great risk to the survivability of the planet—is also being proposed as the best media to 'archive' human accomplishment. We are running out of storage and we are running out of a viable world. Rather than attempt to address these twin problems by adopting more sustainable practices in relation to data production and storage, science just turns to new storage options like synthetic DNA.

Possibly the most hubristic example of this is the ARCH (pronounced Ark—get it?) project—a sort of a high school time capsule writ quite a bit larger. In 2019, the ARCH Mission's Lunar Library crashed onto the surface of the moon. Inside it were tens of millions of pages of documents as well as cells from twenty-fve humans and other organisms and seemingly without irony, it included an open letter from the Mayor of Austin extolling the virtues of queso and urging any and all aliens to stop by and try. In a country that proposes to build walls to keep humans at bay, an open invitation to aliens. To eat queso. I can't even begin to express how many levels of misguided and wrong this initiative is. Starting with the extraordinary vanity that assumes this highly selective but apparently globally representative archive will be in any way comprehensible to future humans—let alone cheese-eating aliens.

Riding on the exhaust fumes of initiatives like this are startups such as LifeShip, which ofers to send personal genetic code embedded in a polymer described as 'synthetic amber', to the moon. The ill-fated Lunar Library was encoded on twenty-fve nickel discs but the ARCH Mission has plans to use archives stored in DNA itself on future missions. I like to say it's putting the thumb back into the thumb drive. Actually, the thumb reference might also explain why early experiments in this particular tech focussed on encoding *The Hitchhiker's Guide to the Galaxy* into DNA sequences.

Recently a huge buzz erupted around a team at Harvard that used CRISPR gene-editing technology to store a 'movie' in the form of bacterial DNA. They started with the image of a splayed hand, inspired by parietal art. Always the nod to foundation myths and preservable practices ('synthetic amber' and cave paintings). But the 'movie', even as a more contemporary reference point, is also more complicated than it might frst seem, and as it turns out, also bears a type of originary thinking.

The scientists chose to use fve frames from what they claim is an 1870s movie of a racehorse, broken down into pixels which were then encoded using DNA (Figures 3 and 4). Their point was not to actually store videos in bacteria but to 'turn cells into historians'. According to one of the researchers: 'We envision a biological memory system that's much smaller and more versatile than today's technologies, which will track many events non-intrusively over time'.25 So the important thing about the experiment was the ability to sequence bacterial DNA in order to reconstruct the pattern of frames that represent change through time.

As a tricked-up flm historian, a large part of me applauds the idea that attention to temporality and change (in any media, including DNA) is a useful thing. Of course, the scientists are not really turning cells into historians. It's more along the lines of creating a new DNA level recording device that will provide retrievable information about the

25 'Scientists replay movie encoded in DNA: "Molecular Recorder" would reveal secrets of brain development' (n.a.), *National Institutes of Health* (July 12, 2017), https:// www.nih.gov/news-events/news-releases/scientists-replay-movie-encoded-dna.

Figs 3 and 4 Image Credit: Wyss Institute at Harvard University. Image by Seth Shipman (June 2020).

progression of cellular processes. This makes the other part of my flm historian self want to address the critical lack of history in this account of video encoding. And another part of me wants to talk about the operating defnition of archiving that all these initiatives seem to rest on.

For the scientists, the images are now a 'movie' (I'm not going to get into the technicalities of why a GIF isn't really a movie), therefore they must have always been a movie. If there is a known problem in flm archiving, it is not the fragility of the media that flms are recorded on—it is the obsolescence of the technological infrastructure. Whenever formats are upgraded, massive migrations need to occur and swathes of dispensable content falls by the wayside.

Film and video archiving is a dynamic, constant process of evaluation, migration and management. None of this is more evident than in the case of British photographer Eadweard Muybridge, the ethically suspect artist who created the horse images used by the Harvard scientists in this exercise (Figure 5). This 'movie' was actually a selection of highly curated still images of a galloping horse named Annie G. and published in Muybridge's *Human and Animal Locomotion*. 26 It is no surprise to learn that late in his career, Muybridge sought to have himself removed from document archives in order to advance the idea that he was the progenitor of the motion picture industry (a claim which is the basis for many debates in the feld of flm history).27 But Muybridge's motivating interest was in dissecting and deciphering movement, not synthesizing it.

Fig. 5 Eadweard Muybridge, *Animal Locomotion, Plate 626* (Washington, D.C., National Gallery of Art, 1887).

26 Eadweard Muybridge, *Muybridge's Complete Human and Animal Locomotion* [1887] (New York: Dover Publications, 1979).

27 Marta Braun, 'Muybridge's scientifc fctions', *Studies in Visual Communication*, 10.3 (1984), 2–22 and Marta Braun, 'Muybridge's Animal Locomotion', *History of Photography*, 24.1 (2000), 52–54, https://doi.org/10.1080/03087298.2000.10443363.

Here then, we have cinema infrastructure itself as obsolescent. For the Harvard scientists, the idea that the infrastructure of moving images itself comes from contingent historical circumstances has been conveniently wiped from memory. This recovered video sequence serves as a metonym for the archive (as I understand it)—*the arbitrary extraction of entities or events from their context, which are teleologically manipulated into a singular progressive structured sequence that produces the illusion of forward momentum and therefore an expectation that there is something 'next'.*

Rather than take this as an opportunity to observe and work within a mangled global entanglement, DNA is used by these scientists as a feld for mining or harvesting categories of time and space that optimistically promise human progression, technological posterity, generational sustainability and individual longevity. And yet as we keep saying, such claims rest on processes that are materially responsible for compromising these ambitions.

**MH:** What we know now and what is revealed to us from every project above as well is that genomics research relies on massive Internet infrastructures and computational power and is imbricated in the extractivist logics that allow and encourage their expansions. Our global communications infrastructure is built on social inequalities that allow for cheap labour, cheap nature, expensive products, proprietary software and hardware, and quick distribution and sales. It is a proftdriven system. Genomics is also powered by proft.

**DV:** And bad history.

**MH:** By way of a conclusion I also want to mention three, generative, non-industry examples for seeing this storage idea through by way of both scientifc realism and speculative design: Karin Ljubic Fister's 2016 scientifc experiments encoding data (like music) into plants;28 *Grow Your Own Cloud* (2018) by Monika Seyfried and Cyrus Clarke (https:// growyourown.cloud/); and *Data Garden* by Kyriaki Goni (https://www. onassis.org/whats-on/data-garden).

When asked by the *New Scientist* (2016) why the DNA of plants were an opportune storage technology, Fister responded:

28 Geof Manaugh, 'Landscapes of data infection', *BLDGBLOG* (February 8, 2016), http://www.bldgblog.com/2016/02/landscapes-of-data-infection/.

I was annoyed about the amount of disc space on my computer. I started wondering, what if I could store data in DNA? It's such an immense reservoir of potential storage—1 gram of it could store over 450 x 1018 bytes. All of the archives in the world could be stored in one box of seeds.29

Similarly, the justifcation for *Grow Your Own Cloud* was to explore the 'the link between two key topics of our time; data and anthropogenic climate change'.30 The idea of storing 'memories' in house plants, and of having a community fower shop be the custodian of preservation (as opposed to an inaccessible data centre) meant a new strategy and an enhancement of consciousness of the current systems that do this. Goni's *Data Garden* works from this idea and asks us to think of 'the future of connectivity beyond surveillance, minimizing the consequences of technological infrastructures on the natural environment'.31 So it seems like artists are at the forefront of exploring not only the environmental impact of DNA's technological infrastructures but also the poetics within the political questions embedded in storage, memory, access and preservation.

**DV:** I really love that these artistic explorations are also poetic in the sense of 'poetic justice'—in which the answer to an environmental problem (the negative efect of DNA data storage for global sustainability) is in fact, the natural environment itself (the DNA of plants and seeds). And once again you've sown that image of a self-enfolding helix in my mind!

#### Bibliography

Australian Wool Network, http://woolnetwork.com.au/dna.html

Beech, Mark 'COVID-19 pushes up Internet use 70% and streaming more than 12%, first figures reveal', *Forbes* (March 25, 2020), https://www.forbes. com/sites/markbeech/2020/03/25/covid-19-pushes-up-internet-use-70 streaming-more-than-12-first-figures-reveal/#6d22fd223104

29 Sean O'Neill, 'Imagine storing all the world's archives in a box of seeds', *New Scientist* (January 13, 2016), https://www.newscientist.com/article/ mg22930560-400-imagine-storing-all-the-worlds-archives-in-a-box-of-seeds/.

30 'Student runner up speculative design award Core77 Design Awards 2019' (n.a.), https://designawards.core77.com/speculative-design/83603/Grow-Your-Own-Cloud.

31 DATAGARDEN (2020), https://www.onassis.org/whats-on/data-garden.

and Facebook', *New Media & Society*, 20.1 (2018), 293–310, https://doi. org/10.1177/1461444816661553

## SECTION 2: ART AND/IN THE ANTHROPOCENE

# 7. Design Education in the Anthropocene:

## Teaching Systems Thinking

*Eric Benson and Priscilla Ferronato*

The following chapter discusses how teaching design through the process of systems thinking, as derived from the disciplines of both ecology and biology, is the best path forward to prevent the worst-case scenarios of climate change. Systems thinking is a process that can help designers to uncover the root cause of a problem and how it connects to the larger picture: people, proft and planet (and everything in between). The conditions of the Anthropocene mean that designers must be able to identify the social, political and environmental repercussions of their work—and take responsibility for them. This process empowers designers to evaluate and shift the emphasis of their outcomes to consider the demand put on our natural resources: where and how we get materials to produce our projects, who and what is afected by our decisions and what will happen to the project after it is implemented. The systems thinking process explored in this chapter is a four-step model (determine project goals, map out the design problem, brainstorm design outcomes and evaluate each possible design outcome) as set forth in the 2017 book *Design to Renourish: Sustainable Graphic Design in Practice*. The authors, who are based at the University of Illinois at Urbana-Champaign, taught this systems thinking model over two years in three diferent courses to test its efectiveness and make improvements to the process, methods, tools and resources from one academic term to the next.

#### Introduction

The Anthropocene is the current geological epoch in which human activity has 'negatively' impacted not only individual ecologies but the entire system of our environmental interactions.1 We have ignorantly pushed Earth out of the Holocene epoch in which agriculture, sedentary communities and, eventually, socially and technologically complex human societies developed.2 The scope, scale and complexity of the self-imposed ecological issues we face today—like global warming, decreasing biodiversity and species extinction, and air, soil and water pollution—have led environmental scholars to suggest the worst is yet to come for our civilization and planet. In fact, a 2019 Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) report from the United Nations (UN) details how the Anthropocene will eventually bring humanity to its knees. The UN study found that '75% of land environment and some 66% of the marine environment "have been signifcantly altered by human actions"' and 'up to 1 million of the estimated 8 million plant and animal species on Earth are at risk of extinction—many of them within decades'.3

Designers and design educators are therefore forced to now confront the fact that they should have acted proactively decades ago (as Victor Papanek prophesied in his 1971 book, *Design for the Real World: Human Ecology and Social Change*) to help prevent the worst from our current

1 Roy Scranton, 'Learning how to die in the Anthropocene', *The New York Times* (November 10, 2013), https://opinionator.blogs.nytimes.com/2013/11/10/ learning-how-to-die-in-the-anthropocene; Will Scranton, Paul J. Crutzen and John R. McNeill, 'The Anthropocene: Are humans now overwhelming the great forces of nature', *AMBIO: A Journal of the Human Environment*, 36.8 (2007), 614–621.

2 Will Stefen et al., 'The trajectory of the Anthropocene: The great acceleration', *The Anthropocene Review*, 2.1 (2015), 81–98, https://doi.org/10.1177/2053019614564785.

3 The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), *IPBES Global Assessment Preview* (April 29, 2019), https://www. ipbes.net/news/ipbes-global-assessment-preview.

potentially dystopian epoch. Additionally, educators must act collectively to realize that furthering the current status quo design instruction will *not* create a viable response to today's eco-crises.4 The development of ephemera and artifacts using our historically taught linear processes (cradle to grave) ignores the natural systems of our planet entirely. This antiquated approach to design education will only hasten the speed of our current downward spiral through the Anthropocene, from which, once we reach the bottom, we may not be able to recover.

However, instead of creating a beautifully kerned apocalypse, design educators could empower their students to create what we want our future to be. Instead of continuing with the current model that increases environmental and consequent social destruction, design educators could use their skills to provide our students with the creative toolkit to regenerate what we took from our Indigenous brothers and sisters, land and waterways. This concept of imagining and creating the future we want will result in what scholars call the 'Ecocene'. Rachel Armstrong coined the term in 2015, having proclaimed at the Urban Ecologies design conference in Toronto: 'there is no advantage to us to bring the Anthropocene into the future. The mythos of the Anthropocene does not help us. We must reimagine our world and enable the Ecocene'.5

We argue that the best way forward to enable the Ecocene is to change our professional practice and teaching to use the process of systems thinking derived from the disciplines of both ecology and biology. Systems thinking is a process that can help designers to uncover the root cause of a problem and how it is connected to the larger picture—people, proft and planet (and everything in between). The conditions of the Anthropocene mean that designers must be able to identify the social, political, and environmental repercussions of their work—and take responsibility for them.6 This process empowers designers to evaluate and shift the emphasis of their outcomes to consider the demand put

4 Rosi Braidotti, *The Posthuman* (Hoboken: Wiley, 2013); Vandan Shiva*, Staying Alive: Women, Ecology and Development* (Brooklyn: South End Press, 2010).

5 Quoted in Joanna Boehnert, 'Ecocene design economies: Three ecologies of systems transitions', *The Design Journal*, 22.1 (2019), 1735–1745 (pp. 1742–1743), https://doi. org/10.1080/14606925.2019.1595005.

6 Joanna Boehnert, 'Anthropocene economics and design: Heterodox economics for design transitions', *She Ji: The Journal of Design, Economics, and Innovation*, 4.4 (2018), 355–374, https://doi.org/10.1016/j.sheji.2018.10.002.

on our natural resources, where and how we get materials to produce our projects, who and what is afected by our decisions and what will happen to the project after it is implemented.

In this chapter, we will illuminate how teaching systems thinking to design students can help them handle a larger degree of complexity and make more responsible outcomes for their projects by considering value creation within a long-term timeframe involving a larger network of stakeholders.7 This chapter will also refect how systems thinking in design is diferent than using more traditional Human-Centered Design (HCD) methods for complex problem solving in design education. We will also discuss how teaching systems thinking to designers encourages a better and more responsible interaction between humanity and the environment (as opposed to, in HCD, where designers are concerned about how to solve only a particular user need).

#### Our Systems Thinking Process

The systems thinking process we used in our courses is grounded in that described extensively in Chapter Two, 'The Hawk, the Squirrel, and the Oak Tree', of the 2017 book *Design to Renourish: Sustainable Graphic Design in Practice*. In their book, Eric Benson and Yvette Perullo divide their systems thinking process into four phases that weave back in forth (circular) to avoid the linear design process (cradle to grave) that has helped place us squarely in the Anthropocene. The process described in the book is uniquely adapted for designers from previous research, namely that by Donella Meadows' *Thinking in Systems: A Primer* and Fritjof Capra and Pier Luigi Luisi's *The Systems View of Life: A Unifying Vision*.

Each of the four *Design to Renourish* phases are written using a language that designers can understand, describing systems thinking as similar to Gestalt theory (the whole is greater than the sum of its parts) and the Charles and Ray Eames' flm *Powers of Ten* (1968; rereleased in

7 Peter H. Jones, 'Systemic design principles for complex social systems', in *Social Systems and Design*, ed. by Gary Metcalf, Translational Systems Sciences 1 (Tokyo: Springer Japan, 2014), pp. 91–128, https://doi.org/10.1007/978-4- 431-54478-4\_4; Birger Sevaldson, *Systems Oriented Design* (2009), http://www. systemsorienteddesign.net.

1977). Gestalt theory and *Powers of Ten* are common topics taught in design foundations and design history courses, and therefore already in the vocabulary of a young design student, making a complex topic like systems thinking easier to understand. Both the term Gestalt theory and *Powers of Ten* demonstrate for the designer how it is best to zoom in and zoom out of an issue to see not only the granular details, but also the larger connected reasons why the issue might exist in the frst place. Systems thinking teaches us that (as environmentalist John Muir stated): '(w)hen we try to pick out anything by itself we fnd that it is bound fast by a thousand invisible cords that cannot be broken, to everything in the universe'.8 In other words, everything is connected on our planet.

The four phases within the systems thinking process were embedded in each of the two projects in our fall 2017 Design Methods course, taught as an exploratory testing ground to introduce the topic to design students. The phases within the *Design to Renourish* process are (in order): *determine project goals, map out the design problem, brainstorm design outcomes and evaluate each possible design outcome*. The general concept of titled phases mimic those used within the design thinking and HCD communities, and therefore can be better understood by designers and disciplines outside of the design community as well. However, one of the biggest diferences from design thinking is that the goals within a systems thinking process is that the outcomes should be circular (like our natural systems) and create zero waste. We defne this as sustainable design, where there is a net zero environmental (and carbon) impact.

Systems thinking, of course focuses on people, as does the more commonly taught HCD approach. Systems thinking, however, is better suited to handle the complex concerns we face in the Anthropocene, as its process tasks the designer with visually mapping out the problem(s) in order to discover the root causes of an issue, instead of identifying solely the symptoms. Within this mapping, the designer not only considers people (the audiences), like in HCD, but also includes the Earth (water, land and air) as a key stakeholder in the process. All of these partners share equal weight and importance, forcing the designer to design circularly (zero waste) using our natural systems as a guide.

8 John Muir, *My First Summer in the Sierra* (San Francisco: Sierra Club Books, 1988), p. 110.

Despite the limitations HCD has in solving the concerns we will face in the current Anthropocene, it contains very useful tools and methods that we taught alongside systems thinking strategies in each of our three courses from 2017–2019. For example, within the second phase of the *Design to Renourish* systems thinking process (*map out the design problem*) students learned how to use storyboards, surveys and user personas, while in the fourth phase (*evaluate each possible design outcome*) students studied SWOT (strengths, weaknesses, opportunities and threats) analysis to better choose the correct possible design direction.

#### Case Study One: Design Methods 2017

In the fall 2017, the authors each taught a section of a required hybrid studio/seminar HCD course directed toward forty total graphic designers, entitled Design Methods. We planned the entire course using the process defned in *Design to Renourish*. Each of the two challenges (as we called the assignments) tasked each team of students to follow our systems thinking design process: *determine project goals, map out the design problem, brainstorm design outcomes*, and *evaluate each possible project outcome* to solve a social or environmental issue that afects humanity. During the process, the students used HCD tools like storyboards, surveys, user personas, and a SWOT analysis to help make collective decisions and designed outcomes that embrace nature's cyclical model. The goal of each challenge was to make the students capable of leading system change initiatives addressing complex sustainability, ofering fexible access to the best learning experiences, tools and case studies.

Throughout this course we did not mention the topic of Anthropocene, despite it being implied in every one of the assignments. We hoped that if we focused on the future we wanted to have (Ecocene), we could limit apathy and paralysis in decision-making based on despair.

The course structure and lecture materials were exactly the same in both sections so we could get a larger consistent sampling of student feedback and outcomes to see how efective or inefective the systems thinking process was. We organized the ffteen-week semester into three equal chunks. The frst fve weeks ofered diferent viewpoints on design through readings/discussion, introduced systems thinking and sustainable design methodologies, and conducted workshops with various tools and methods used by designers that focus on HCD and 'Thinking Wrong'.9 The remaining ten weeks were broken into two fveweek challenges that asked the students to use what they learned in the frst fve weeks to fnd and propose solutions to social and environmental challenges.

The two challenges stressed the importance of locality. Each proposed project by the student design teams must be, in the end, realized outside of the classroom on campus or in the community. We championed the local, so the student teams could more easily interview people, would know the area better and its issues, and could see the results (at the end of the project) in person. Most importantly, they could see themselves as part of the community they were designing for (and with) and feel greater agency throughout the process knowing that the fnal solution would hopefully also positively their lives as well.

The frst challenge we assigned was entitled 'Local Resources', where each team was asked to pick one of the following four subtopics to explore: plastics, water, food or waste. We set fairly strict parameters in this frst systems thinking project, allowing the teams access to only these four questions connected to the sub-topics provided: How can we reduce plastic consumption (on college campuses or in the local community)? How can we use design to eliminate contaminants in the local drinking water supply? How might we dramatically reduce waste by transforming our relationship with food in the community or on campus? How might we establish better recycling habits on campus?

The frst challenge allowed the student teams to pick an area to explore. However, since this was the frst time almost everyone in the studio had used systems thinking to design, we curated the questions in order to eliminate any extra confusion caused by a completely wide open prompt coupled with a new way of creating. But in the second challenge, we included a more open design prompt, since we felt that the experience with systems thinking in the frst challenge would better prepare the students for a project of their choosing.

At the end of the frst challenge, we took a portion of the following class period to debrief. We asked each class section the same questions about what they learned from using systems thinking in design and

9 John Bielenberg et al., *Think Wrong: How to Conquer the Status Quo and Do Work That Matters* (San Francisco: Instigator Press, 2016).

what could be improved upon for the next project. Overall, the students felt that using systems thinking when designing was incredibly important. They were confused along the way—as it was very new to them. However most confrmed that 'confusion was good'. Discomfort forced them not to fall into the trap of creating the same outcomes that solved one problem but created many others. With understanding the bigger picture of the design problem, they felt they could develop a better and more sustainable solution. We used what we gleaned from these two course conversations to make small and quick changes to the second challenge to improve upon the overall class experience.

This second challenge was curated into four main topics: health, education, democracy and the planet. We chose these areas as they are all afected by and have helped create the Anthropocene epoch. To help get the design teams thinking, we provided some example questions in each of the four themes. For example, in health, we provided this example project query, 'How might we make the Emergency Department (ED) waiting room more humane and user-focused?' while in the democracy issue, we furnished an idea asking 'How might we design an accessible election experience for everyone? How can we more engage people to vote?'

The students produced some interesting solutions for each of the two challenges in Design Methods that could be implemented with the possible acquisition of further funding sources but did not make tangible artifacts as they were lacking the time and monies to purchase the sustainable materials needed to have a net zero impact. Instead, the students turned in proposals in the format of digital presentations. Most of the solutions, in general, involved a digital outcome like a website or mobile application. These solutions were not necessarily driven by the lack of money or avoidance of physical solutions (to reduce environmental impact), but instead were based on a careful consideration of the efectiveness of a digital solution (in comparison to others brainstormed) after going through the entire four phase systems thinking process as a team.

At the end of the fall 2017 semester, we collected written and verbal feedback from the students to create an in-depth analysis of the project outcomes in order to tweak the syllabus and general approach for the next time the course (or similar class) was taught. The analysis verifed how much students understood about systems thinking and how and why they would include it in future school and professional design projects.

Specifcally, this is the feedback that was most commonly given regarding the semester spent thinking in systems. In the frst *determine project goals* step from *Design to Renourish*, it is important to suggest to the students to reach out to and speak with as many people connected to the design problem as possible. Many times, they didn't locate an important expert or community leader until later in the project, changing the way they would have approached a solution at the outset. A more experienced systems thinking team of designers will learn to research better to avoid missing connections.

In the second *map out the design problem* step, we found after mapping out the design problem, it was smart to suggest to the students to invite fresh eyes to the map to locate opportunities and issues that the design team missed. The more details and information the map holds the better it will serve the design team in choosing a fnal solution(s). We also confrmed that using user personas was vital for properly understanding the people involved. Interviews with actual people and surveys helped develop the personas.

In the third *brainstorm design outcomes* step, we found that if we used a strategy from the book *Think Wrong* by John Bielenberg, Mike Burn, Greg Galle and Elizabeth Evitts Dickinson, the design team could really separate themselves from standard design outcomes and fnd solutions that could solve the problem in ways that followed nature's cycle of 'no waste'. Specifcally, the *Think Wrong* 'Moonshot' exercise10 was incredibly valuable as a tool asking the students to not constrain themselves by self-imposed parameters. This exercise asked the students to put forward potentially crazy concepts to solve a design problem. This pedagogical strategy helped the students not continue in the status quo design concepts they are used to proposing for a standard design project. They thought in a creative childlike mindset, where they began to unlearn the outdated linear design process and gave them hope that the Ecocene was indeed possible. After the 'Moonshot' exercise, the design teams created storyboards of their ideas to further detail out their plans to fnd issues and strengths in their solutions.

10 "When to Use the Think Wrong Moonshot Drill." Moonshot Drill, Solve Next, 2021, solvenext.com/moonshot.

In the fourth and fnal *evaluate outcomes* step, we found that using a more typical SWOT analysis tool was very useful in scrutinizing each possible solution to the problem. In combination with the SWOT analysis, the students had to also look at each solution in relation to the triple bottom line. They asked how each solution could create an experience that minimizes resources used and best use materials and vendors that were sustainable and used renewable energy. This was challenging of course, and many teams lacked experience or time to really locate the right materials. This was a clear opportunity for improvement in regard to what resources we provide to the students for this type of systems thinking project. Our thoughts headed into further courses, was to purchase a license to Life Cycle Analysis (LCA) software to help the designers visualize the actual environmental footprint of every material chosen in their design solutions.

This process of systems thinking defned by the book *Design to Renourish* was also tested in the fall of 2018 and spring of 2019 with two diferent design courses, with tweaks in the implementation learned from the fall of 2017 iteration.

#### Case Study Two: Fall 2018

Based on the students' feedback from the previous semester, the Design Methods course in fall 2018 (composed of, this time, only twenty undergraduate students) was organized in a way to make students more comfortable with the circularity of the systems thinking process. Undergraduate students want a syllabus that describes every activity assigned during the semester in advance, which, we found, is not possible when working in a systems thinking approach. Appropriately, too many times, designers must stop, zoom in or zoom out during this circular process, making new work and research segues that are often unpredictable from the outset of the assignment. Therefore, students feel unprepared at times for what is coming next. It is important to include this fact on the syllabus and state it vocally in class.

This Design Methods course was again divided into two challenges. This time, Priscilla Ferronato decided to build upon the previous course iteration by including real world cases with community partners. They were the Krannert Art Museum (KAM) at the University of Illinois at Urbana-Champaign and the Champaign County 4-H Club. The decision to collaborate with these organizations was an attempt to further demonstrate how to apply systems thinking in real world projects whose tangible outcomes can serve as an incentive to grow the students' interest into systems thinking. The course followed the same structure as the previous semester in terms of the lectures' topics and recommended readings. The biggest change was to shorten the introductory fve weeks down and instead create two eightweek projects with the previous exercises from the initial fve weeks embedded in the frst assignment.

The goal of the frst project was to redesign the communication experience of the Krannert Art Museum to the community. The students were required to present to the client at mid-term and, of course, at its completion deadline. The goal of the mid-term presentation was to collect feedback from the 'clients' including the Director and Marketing Department at KAM. However, as part of the systemic approach, the students also needed to identify who the other stakeholders were in connection to the museum. When the designers identifed other stakeholders (such as members of the local community, tourists, students, employees and service providers), they began to zoom out to view the causes and consequences of the 'identifed problem'.

Not only human stakeholders were taken into consideration: during the stakeholder mapping activity it was necessary to identify other elements of the system that humans may interact with, like the planet. This was the most challenging of all the initial activities for the students, as they wanted to rush to create tangible solutions before measuring and evaluating the impacts on people and the ecosystem. Based on the *Powers of Ten*, students were asked to 'zoom out' ten times and expand their design problem and solution, and also 'zoom in' ten times to narrow it down. This exercise provided meaningful insights for the development of systemic solutions. (This was something new we added from the frst Design Methods course.) It is important to mention that as soon as the students were able to map out the elements of the system (and especially their interactions), more sustainable solutions were developed. Everyone found that visualizing the connections between the many elements of the systems provides a way to make the opportunities tangible.

The second project, in partnership with Champaign County 4-H Club, followed the same structure as the frst assignment. The students were asked to meet with the Director of the program both at the midterm and fnal presentations. However, since the goal of this project was to design an experience to promote the 4-H program to increase the interest of children and teenagers in Science, Technology, Engineering, the Arts and Mathematics (STEAM), this project required a higher number of interactions with diferent stakeholders than the students initially thought. As soon as the students identifed the diferent stakeholders and elements in the systems of interaction, it was easier to know who to contact and consequently understand their thoughts and opinions about the 4-H club and STEAM.

The most important fnding for the students was that after using systems thinking, they realized how complex a simple visual communications project should be. With all the important yet diferent stakeholders, the students and Ferronato felt the best path forward was to 'design with not for'. Therefore, Ferronato decided to introduce the concept of participatory design as a method to also incorporate into systems thinking. Some of the design teams took this opportunity to develop co-design sessions with the stakeholder groups to improve the project outcomes. The design teams that ran co-design sessions received better feedback at their critiques from the 4-H Club and faculty than those who decided against running workshops.

#### Case Study Three: Spring 2019

Another goal of this research was to also test teaching systems thinking in a design course that did not include a component focusing on HCD tools and methods. We decided to use one section of the junior level required course entitled Ethics of a Designer in a Global Economy (EDGE) as another testing ground. The students in this ethics class were introduced to systems thinking in the fall 2018 Design Methods course, so we were interested to learn how much they remembered, how much of the process we would have to repeat and how much deeper in detail we could go if students were already on the right track. Benson taught this course with help from a teaching assistant (Miriam Salah) that also co-taught with Ferronato in the fall 2018 version of Design Methods. The teaching assistant was therefore also aware of the subject matter and the systems thinking process, making the transition easier. Much like in the previously mentioned case studies, though the Anthropocene was an underlying theme, the course objectives were guided by the Ecocene model. We felt that an Ecocene approach that looks toward a hopeful future, rather than a fearful and catastrophic one, would better inspire students to seek out imaginative and creative design solutions.

This course was composed of eleven students and was divided into four projects that focused on ethics within the design feld and also how designers connect to the current global issues facing humanity. In particular, we structured the course around ethical discussions and projects starting from our own personal values to how these values connect to a larger community and, consequently, the planet. This class organization led us to focus exclusively on environmental issues and climate change (diferent from the frst two Design Methods courses).

The frst two projects in EDGE were concerned with the individual students' own personal code of ethics and how that would carry forward and afect the fnal two projects. The frst project asked the designer to choose a breakfast, lunch or dinner recipe that carried a signifcant amount of emotional signifcance to them and cook or bake it for a class potluck the following week. At the meal, we discussed the ethics connected to organic food, vegetarianism, veganism and the carbon footprint of what we all eat. This conversation was infused with readings (consumed the night before) on food justice and ethics in general. After the potluck and discussion, the students were assigned with creating an information graphic (in a relevant format of their choosing) that demonstrated the carbon footprint of the transportation needed to make their recipe.

This project resulted in some stimulating outcomes and the topic of 'personal values' was the segue to the second project where the students wrote their own personal code of ethics and designed individual personal brands that represented what they stood for as a citizen and designer. The course was designed in this fashion, attaching one project to the next through a similar theme. Therefore, the third project focused on ethics from the standpoint of a community that depended on the actions of one's personal value system (connecting it to both project one and two). This third project asked the students to use systems thinking to reimagine a delivery mechanism for a sustainable product or service that was important to them. Here, the designers were introduced to the concept of a circular economy, where objects and services are created and distributed with zero waste and no carbon footprint (again, emulating nature's model of function).

We re-read *Design to Renourish*'s chapter on 'The Hawk, the Squirrel, and the Oak Tree' to reinforce what this group of students had already learned in their previous Design Methods course and input an additional two readings and four informational videos visualizing and explaining the circular economy (that included case studies). The students used this prompt to research similar or inspirational examples of circular products and services to infuence their own design. The end result of this project was a proposal in the form of a website mockup or animated video that explained their idea clearly and was, of course, visually interesting and convincing.

The fourth project that followed this circular economy assignment replicated a portion of the second challenge from the frst version of the Design Methods course (2017) asking here for the students to focus on the theme of 'the planet' in an attempt to better connect the everyday American to climate change concerns. We asked the students to read both a portion of the philosophy and ethics book *One World Now* by Peter Singer and the seminal 1999 article on climate change by Dr. Michael E. Mann, 'Northern hemisphere temperatures during the past millennium: Inferences, uncertainties, and limitations' to locate opportunities (using a systems thinking process) for better communication of the clear and present danger that climate change poses to our civilization.

In this third instalment of teaching systems thinking, we learned that the students had indeed unfortunately lost much of the knowledge gained about systems thinking from their previous Design Methods course. There was a real struggle in re-explaining the systems thinking process to the designers during each of the fnal two projects—where many only vaguely remembered reading Chapter Two from *Design to Renourish* and using the systems thinking process in their previous Design Methods challenges. We did not do as deep of a dive in collecting qualitative feedback in comparison to the frst attempt within the 2017 Design Methods course, as the sample size of eleven (compared to forty) was fairly small. We did, however, glean enough from the weekly desk critiques and one-on-one discussions, that there was a lack of information retained about systems thinking prior. It leads us to believe that systems thinking must be continually reinforced throughout the design curriculum, and not appear only in one class.

#### Conclusions

The results from each diferent course section demonstrated that systems thinking was initially a very foreign and overwhelming way to solve problems, causing confusion. It also 'forced' the students to work outside their comfort zones. However, students understood the importance of the process contrasted against the current and future global problems we all face. One of the key takeaways from the Design Methods course was to eliminate the fve-week grouping of exercises and instead embed them into the two eight-week challenges. This worked more seamlessly and could also be implemented in the structure of the Ethics (or any) course as well in the next iteration.

Overall, the readings, lectures and project setups seemed to be understandable, interesting and challenging for the design students. And, as expected, the frst team challenge from the 2017 Design Methods course was more confusing (and took longer) than the second, as they slowly became accustomed to this new way of thinking and working. Continually adding systems thinking into every design course in a department's curriculum will help remedy the problems associated with initial confusion about the newer creative process and aid in the retention of the core materials and methods.

There were clear areas for improvement in teaching the topic and opportunities to add new tools, methods (like co-design) and exercises to the class. Moreover, the use of systems thinking encouraged students to think beyond the insights obtained through the use of the HCD methods. Although it is not possible to say that the use of systems thinking will alone be able to reverse the climate impact of our design activity, it can be considered a more responsible approach (as seen through the projects presented by the students in our classes and beyond). The systems thinking process is a frst step toward improving design education and potentially avoiding the worst-case scenarios in the Anthropocene, instead imagining a better future (or an Ecocene).

We must design using systems thinking, and teach systems thinking, now. We are racing against the clock to prevent the worst of the possible catastrophic environmental, social and economic outcomes driven by human-made climate change. The recent 2018 Intergovernmental Panel on Climate Change (IPCC) report demands for us to level our greenhouse gas (GHG) emissions by 2020 and cut them nearly in half by 2030 to avoid irreparable damage to the climate and ourselves. The Anthropocene is upon us and systems thinking through design is the best path forward to imagine a future we would all like to have.

Although the systems thinking curriculum we implemented with our students focused on net zero (sustainable) impacts on the planet, we believe the design solutions going forward should be regenerative in outcome. Regenerative design, by defnition, encourages participation with living systems in ways that increase systemic vitality.11 As the IPCC report suggests that we must repair our climate, so too should we also clearly design in a way that helps give back to what has been stripped historically from nature to make our posters and toasters. To drawdown our greenhouse gas emissions, we must regenerate the Earth to revitalize its soil and plant life to help with the eforts to reach climate stability. This is an area for designers that is also ripe for exploration (like systems thinking) as it is an emerging research area, led by holistic scientist and designer Daniel C. Wahl.

We hope that this chapter is a valuable and useful case study for you to explore within your own design classroom. It is clear that due to global warming, overconsumption of our fnite resources, and air/land/ water pollution, design must change from a linear process to one that is circular. Instead of contributing to these problems, design and design education must become an agent for change to remedy these issues and renourish people and the planet instead.

11 Daniel Christian Wahl, *Designing Regenerative Culture* (Axminster: Triarchy Press, 2016); Kate Raworth, *Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist* (London: Random House, 2017).

#### Bibliography

# 8. Inspiration from Goethe's Tender Empiricism:

How to be the Person Collecting, Analyzing and Visualizing Data

*Joshua Korenblat*

Science, humanities and design might seem like unrelated felds. Yet, information designers, who unpack complex data involving real-world issues, can beneft from the ability to synthesize these seemingly disparate practices. To learn more integrated, humanistic approaches to data visualization, we might look to a time when science and the arts were less divided. The following chapter focuses on poet-scientist Johann Wolfgang von Goethe, the Romantic-era polymath. Goethe called his scientifc method 'tender empiricism', a complementary practice to analytical empiricism. Goethe believed in portraying the same phenomena under subtle, changing conditions. While observing, collecting and visualizing, he also searched for what might be missing. A plant, for example, is not a collection of parts; it also portrays the process of growth even in static form. For Goethe, observational discoveries can change the inquiring mind. In contrast to data visualization practice today, which often focuses on summaries and abstract charts, Goethe believed that authentic, insightful truth dwells in real-world details. The second half of the chapter illustrates how Goethe's 'tender empiricism' can be applied to design pedagogy. These case studies show how a Goethean ecological approach can be used to model a more ethical way of working with data.

#### Introduction

Scientists study nature. Humanists study human experience. Designers make the artifcial world. Yet today, in sustainability and digital humanities projects, these long-divided camps collaborate and co-create. For instance, when planning and implementing a sustainability project that involves data visualization and communication to the public, teams think like scientists: they collect information and analyze it, and conduct research in a structured way. To represent and communicate the data, teams must think like humanists. They try to adopt the perspective of their audience, with empathy for how it feels *not* to understand a topic. From this insight, designers determine how they present information, from simple to elaborate, and how much to guide readers with explanatory prose and pictures. Even as they design the fnal product on digital platforms, teams also need to remain attentive to how humans evolved to read their natural environment. These principles translate into how we read charts and maps, 'landscapes' of data, in the blink of an eye. In the end, the felds of science, the humanities and design all inform the work of interdisciplinary teams, in support of the most efective, human-centered projects.

For these sustainability teams, we need interdisciplinary people too. Each individual of the team embodies the interdisciplinary ethos. To look for role models for this type of interleaved work, we can acquaint ourselves with a time before scholars and makers specialized. Here, poets could study science and scientists could learn from poets. Visualization, meanwhile, wasn't just a tool for building abstract charts and applicable insights. Through the 'mind's eye', visualization could also be a tool for beholding and perceiving phenomena without abstract concepts in the way. This provided the beholder a sense of philosophical and spiritual well-being—and also practical benefts in creating richer inquiries.