Scenario Plans (& Delphi Research)

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Beyond Moore’s law, Beyond Silicone Chips

Beyond Moore’s law (by Dr Ed Jordan)

After almost 60 years, Moore’s law, related to the doubling of computing power every year-and-a-half-ish, still holds. At the current exponential speed, there is a brick wall looming in the foreground: the physical limitations of silicon chips. The most straightforward example of how that might impact a company is to look at Intel Corp. But first more on Moore’s law and the more general idea of learning curves.

At some point, the semiconductor material currently in use will no longer support the improvements necessary to meet the requirements of an expanding Information Technology infrastructure. In times past, the way Moore’s law characterized how the growth in the density of computer chips was at the heart of the IT revolution, we have experienced. Interesting, Moore himself viewed this so-called law as an observation, not some grand organizing principle that helps us understand a set of observations that have stood the test of time with many replications. The reason we speak of Newton’s Third Law of Motion, for instance, as a Law is because of the profound success in explaining what we now call Classical Mechanics (Newton’s Laws of Motion, 2020). On the other hand, Moore simply graphed the growth of the number of components on computer chips vs. time using semi-log paper. While the resulting straight line is striking, it does not explain some underlying physical phenomenon (Jordan, 2010). For this reason, the purists among us will always refer to this observation using the lowercase form of law. Below is a picture of the original presentation that Gordon Moore made in 1965 of the characterization (Rhines, 2019).

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Figure 1: Moore’s law

Figure 1: Moore’s law, as presented in 1964. From Predicting semiconductor business trends after Moore’s law. Used with permission

The impact that Moore’s observation on the semiconductor industry proved to be more of an organizational imperative than an organizing principle that explains a body of observations. As Rhines (2019) points out, over time, Moore has added serval caveats to the original observation.

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Figure 2 Moore’s caveats vs time

Figure 2: Moore’s caveats vs time. From: Predicting semiconductor business trends after Moore’s law. Used with permission

Recently, I had the great privilege of attending a webinar hosted by SemiWiki (Rhines, 2020). In this Webinar, Walden Rhines presented where he thought the semiconductor industry was going, what could be called the Learning Curve Roadmap to device improvement, and when some new material may be required to replace the current technologies. Dr. Rhines suggested that if one considers the mathematics of the learning curve, plotting revenue per transistor vs. cumulative transistors shipped using a log-log graph, you have a better picture (Figure 3) of where the industry has gone than the original Moore’s law.

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Figure 3: Log-Log Predicting Semiconductor Business Trends

Figure 3: Log-Log View: From: Predicting semiconductor business trends after Moore’s law. Used with permission

The idea of learning curves takes several forms (Learning Curve, 2020). From the shape of the plot presented by Rhimes (2019), it appears what was meant was a power-law relationship between learning and experience. With the correct selection of coefficients, one can imagine a cure, such as we see in Figure 4 (Learning Curve, 2020).

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Figure 4: Learning vs Experience

Figure 4: Learning vs Experience – Power Law: From https://en.wikipedia.org/wiki/Learning_curve

As part of the discussion during the webinar, Rhimes goes on to show (Figure 5) that if you plot (log-log) cost per function vs. time (Rhines, 2019, p. 20), you see the same kind of straight-line suggestive of a power-law relationship between learning and experience (Learning Curve, 2020).

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Figure 5: Cost over Time

Figure 5: Normalized Chip Cost. From: Predicting semiconductor business trends after Moore’s law. Used with permission

Rhimes further suggest that it is instructive to think of semiconductor trends in terms of the so-called Gompertz Curve (Figure 6). This curve has a characteristic “S” shape and is often used to predict saturation, among other things (Rhines, 2019, p. 22).

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Figure 6: Gompertz Curve

Figure 6:Gompertz Curve. From: Predicting semiconductor business trends after Moore’s law. Used with permission

The Gompertz Curve is a specialized application of the sigmoid function, again from the mathematics of learning (Learning Curve, 2020). As described by Rhimes, the so-called point of inflection of the Gompertz Curve is particularly interesting (Figure 7).

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Figure 7: Lifecycle S Curve

Figure 7: Lifecycle S Curve. From: Predicting semiconductor business trends after Moore’s law. Used with permission

In the context of this discussion, this particular application suggests the point that growth of the cumulative unit volume of transistors will begin to slow. This point is around 2038. As the title of Figure 8 suggests, that is the point at which a new technology will be necessary.

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Figure 8: Projected Transition Point

Figure 8: Projected Transition Point. From: Predicting semiconductor business trends after Moore’s law. Used with permission

In 2010, Jordan found that a new technology should emerge in the timeframe of 10 to 30 years (which should be before 2040). What constituted emergence was also defined in the study. Emergence was defined to mean that point when a prototype would be available for full-scale testing (Jordan, 2010). This definition seems to fit comfortably in the construct, as presented during the webinar (Rhines, 2019). The work by Rhimes (2019) and Jordan (2010), separated by almost a decade, suggest approximately the same answer. By about 2040, a new technology should emerge that will ultimately replace the semiconductor materials that are in current use today. What is important is that these two lines of reasoning, using profoundly different approaches and divergent frames of reference, arrive at essentially the same conclusion. This convergence suggests the confirmability of both.

So what does this have to do with scenario planning in general, and what does that mean for chip companies like Intel? And, what will the likely disruptive technology be that replaces the silicone chip? Jordan (2010) identified likely disruptive technologies.

We at ScenarioPlans.com previously talked about Intel in these two blog posts: how the future of computing is taking on a life of its own, as well as, outa time — the tic toc of Intel and modern computing. It will be interesting to see if Intel remains standing in 2040 or 2050.

References

Jordan, E. A. (2010). The semiconductor industry and emerging technologies: A study using a modified Delphi method (3442759) [Doctoral dissertation, University of Phoenix]. UMI.

Learning curve. (2020, April 7). WIKIPEDIA: The Free Encyclopedia. Retrieved April 21, 2020, from https://en.wikipedia.org/wiki/Learning_curve

Newton’s laws of motion. (2020, March 8). WIKIPEDIA: The Free Encyclopedia. Retrieved April 19, 2020, from https://en.wikipedia.org/wiki/Newton’s_laws_of_motion

Rhines, W. (2019). Predicting semiconductor business trends after Moore’s law. A SemiWiki.com Project. https://semiwiki.com/

Rhines, W. (2020, April 1). Predicting semiconductor business trends after Moore’s law [Webinar]. SemiWiki. https://app.gotowebinar.com/unified/index.html#/webinar/4339831074971239694/attend/3541623017104899086

The Future of Computers and Quantum Computing Part Duex

On April 4, 2019 the DC chapter of the IEEE Computer Society Chapter on Quantum Computing (co-sponsored by Nanotechnology Council Chapter) met to see a presentation by and IBM researcher named Dr. Elena Yndurain on the subject of recent efforts by that company in the realm of quantum computing. I was fortunate enough to be able to attend. I was hoping the presentation would be technical enough to be able to better understand the basics of quantum computing in the sense of a future time-line of when this new technology would be ready for the market place as defined during the course of my own research (Jordan, 2010) which is to say that a working prototype would be ready for full-scale testing. I was disappointed.

During the set-up for the real purpose of the talk, the presenter stated that the phases of quantum computing could be thought of as being in three phases of increasing complexity: (a) quantum annealing; (b) quantum simulation; and, (c) universal quantum computing. Ultimately, the goal would be (c). But the current state of the technology is (a).

It was also stated that there were essentially three possible technologies for quantum computing: (a) super conducting loops; (b) trapped ions; and, (c) topological braiding. Both (a) and (c) require cryogenic cooling. The IBM device uses technology (a) that is cooled down to 15 miliK0 (whew!). Technology (b) involves capturing ions in an optical trap using lasers. This technology operates at room temperature but suffers from a signal-to-noise problem that (a) does not. Technology (c) was not discussed.

The IBM device is a 50-qubit machine. The basic functionality of the device is predicated on Shor’s algorithm (Shor’s algorithm, 2019) and Grover’s search algorithm (Grover’s algorithm, 2019). These mathematical algorithms were developed during the 1990s. They are complex functions so there is a real part and an imaginary part. When queried the presenter stated the gains achieved by this so-called quantum annealing device were from the simplicity of the computation not the speed of the processor. The presenter went on to say that the basic algorithms had been coded in Python (Python (programming language), 2019).

That the IBM device is based on a 50-qubit processor struck me as being a bit coincidental. Recall from my first post on this subject, there has been an effort (by some unidentified group) to develop a fault-tolerant 50-qubit device since 2000. As of the publication of the paper this had not been achieved (Dyakonov, 2019). When I asked about this, the presenter simply stated that the IBM device was fault-tolerant but declined to offer any specific statistically based response. It should be stated that, during the presentation, Dr. Yndurain remarked that information included was cherry-picked [my words, not hers] to put things in the best light. Why?

During the presentation, what became clear is that IBM is building an ecosystem around the 50-qubit device. They have rolled this thing about as the “Q” computer. In order to gain access to the device, researcher must “subscribe” to the IBM service or simply “get in the que”. One also has to go through a training/vetting process to be able to develop the particular program the researcher needs to solve a particular problem. Seriously?

It seems to me this leaves two fundamental questions on the table: (a) will quantum computing be the next great disruptive innovation that supplants silicone dioxide (Schneider, The U.S. National Academies reports on the prospects for quantum computing, 2018) (Schneider & Hassler, When will quantum computing have real commercial value? Nobody really knows, 2019) (Simonite, 2016); (b) What was the point of the presentation?

My answer to the first question is that I remain skeptical. When queried, the presenter said that the materials used were proprietary and would not be available for use by the audience. I will also say that there was a notable lack of specific information in the presentation materials that could be verified. This suggests the answer to the second question: the point of the presentation was a sales pitch. IBM seems to be building an ecosystem around this 50-qubit device that will solidify market share for what was admittedly the very earliest stage of quantum computing. IBM seems to be continuing in the tradition of Moore’s law being a social imperative not a physics-based phenomenon.

References

Dyakonov, M. (2019, March). The case against quantum computing. IEEE Specturm, pp. 24-29.

Grover’s algorithm. (2019, April 5). Retrieved from Wikipedia: https://en.wikipedia.org/wiki/Grover%27s_algorithm

Jordan, E. A. (2010). The semiconductor industry and emerging technologies: A study using a modified Delphi Method. Doctoral Dissertation. AZ: University of Pheonix.

Python (programming language). (2019, April 7). Retrieved from Wikipedia: https://en.wikipedia.org/wiki/Python_(programming_language)

Schneider, D. (2018, Dec 5). The U.S. National Academies reports on the prospects for quantum computing. Retrieved from IEEE Spectrum: https://spectrum.ieee.org/tech-talk/computing/hardware/the-us-national-academies-reports-on-the-prospects-for-quantum-computing

Schneider, D., & Hassler, S. (2019, Feb 20). When will quantum computing have real commercial value? Nobody really knows. Retrieved from IEEE Spectrum: https://spectrum.ieee.org/computing/hardware/when-will-quantum-computing-have-real-commercial-value

Shor’s algorithm. (2019, April 7). Retrieved from Wikipedia: https://en.wikipedia.org/wiki/Shor%27s_algorithm

Simonite, T. (2016, May 13). Morre’s law is dead. Now what? Retrieved from MIT Technology Review: https://technologyreview.com

The Future of Computers and Quantum Computing

Do you know what Gordon Moore actually said? In 1965 Gordon Moore observed that if you graphed in the increase of transistors on a planar semiconductor device using semi-log paper, it would describe a straight line. This observation ultimately became known as Moore’s law. The “l” is lower case in the academic literature because the law is not some grand organizing principle that explained a series of facts. Rather it was simply an observation. Moore adjusted the pronouncement in 1975 to set the vertical scale at every two years (Simonite, 2016). This so-called law has been the social imperative that has fueled innovation in the semiconductor manufacturing industry for well over 50 years. But it was a social imperative only (Jordan, 2010). It was clear from the beginning that the physics of the material would eventually get in the way of the imperative.

There is a physical limit to how far you can shrink the size of the individual devices using silicon dioxide, the underlying material of which all our electronics is made. That limit appears to be about 10 nanometers (Jordan, 2010; Simonite, 2016). There are also other more practical reasons why this limit may be unachivable such as heat disapation (Jordan, 2010). Although, given the cell phone industry seems to be driving the technology of late, significant strides have been made in reducing power consumption of these devices. This lower power consumption implies less heat generation. It also seems to imply getting away from a purely Van Neuman computational architecture toward a more parallel approach to code execution.

This brings us to the fundamental question: what technology is next? When will that technology emerge into the market place? My own research into these questions resulted in some rather interesting answers. One of the more surprising responses was the consensus about what was meant by emerging into the market place. The consensus of the Delphi panel I used in my research was when there was a full scale prototype ready for rigorous testing (Jordan, 2010). One of the most surprising answers addressed the consensus about what the technology would be that replaces silicon dioxide. My research suggests the replacement technology would be biologic in nature, RNA perhaps? The research also suggests this new technology would certainly emerge within the upcoming 30 years (Jordan, 2010). Given the research was conducted nine years ago, this suggests the new technology should be ready for full-scale prototype testing in about 20 years from now. I will address why this time frame is of significance shortly.

It turns out that this question of using RNA as a computational technology is being actively investigated. It would be difficult to predict to what extent this technology may mature over the next 20 years. But, in its current state of development, the computational speed is measured on the scale of minutes (Berube, 2019, March 7). Ignoring the problem of how one might plug a vat of RNA into a typical Standard Integrated Enclosure (SIE) aboard a US submarine, speeds on that scale are not particularly useful.

The Holy Grail of the next generation of these technologies is undoubtedly quantum computing (Dyakonov, 2019). There seems to be a lot of energy behind trying to develop this new technology with a reported “…laboratories are spending billions of dollars a year developing quantum computers.” (Dyakonov, 2019, p. 26). But we are left with the same question of when? Dyakonov divides projections into optimistic and “More cautious experts’ prediction” (p. 27). The optimists are saying between five and 10 years. The so-called more cautious prediction is between 20 and 30 years. This more cautious realm fit with my research as well (Jordan, 2010).

The real problem with achieving a working quantum computer is the shear magnitude of the technical challenges that must be overcome. In a conventional computer, it is the number of states of the underlying transistors that determine the computational ability of the machine. In this case a machine with N transistors will have 2N possible states. In the quantum computer, the device is typically the electron that will have a spin of up or down.  The probability of a particular electron spin being in a particular state varies continuously where the sum of the probability of up and the probability of down equaling 1. The typical term used to describe a quantum device used in this way is the “quantum gates” (Dyakonov, 2019, p. 27) or qubits. How many qubits would it take to make a useful quantum computer? The answer is somewhere between 1,000 and 100,000 (Dyakonov, 2019). This implies that to be able to make useful computations a quantum machine would have to something on the order of 10300 qubits. To illustrate how big a number that is I quote: “it is much, much greater than the number of sub-atomic particles in the observable universe.” (Dyakonov, 2019, p. 27). The problem is that of errors. How would one go about observing 10300 devices and correcting for errors? There was an attempt in the very early years of this century to develop a fault-tolerant quantum machine that used 50 qubits. That attempt has been unsuccessful as of 2019.

The basic research being done is of considerable value and much is being learned. Will we ever see a full-scale prototype ready for rigorous testing? I am beginning to doubt it. I am of the opinion that a usable quantum computer is not unlike controlled fusion: the ultimate solution, but always about 10 years out. So next year, our quantum computer (and controlled fusion for that matter) will not be nine years out but still another 10 years.

 

References

Dyakonov, M. (2019, March). The case against quantum computing. IEEE Specturm, pp. 24-29.

Jordan, E. A. (2010). The semiconductor industry and emerging technologies: A study using a modified Delphi Method. Doctoral Dissertation. AZ: University of Pheonix.

Simonite, T. (2016, May 13). Morre’s law is dead. Now what? Retrieved from MIT Technology Review: https://technologyreview.com

 

 

Salt and Battery, When does Storage make Fossil Fuel Obsolete

Last week the world’s biggest Electric Vehicle (EV) battery company made a big opening splash on its IPO. CATL is a Chinese company that IPOed with a massive 44% pop on open. The company offered up only 10% of the shares in the IPO, valuing the company at more than $12B. China has limits on how much a company can IPO at (price based on PE ratio) and a 44% limit on the amount an IPO can rise in first day of trading. Expect this company to jump continually for some time. CATL is now the largest EV battery company in the world, primarily with lithium-ion for autos.

Of course, you can just use power as needed, when needed. With the rapid increase in efficiencies of wind (where the wind blows) and solar (where the sun shines) this is becoming ever-more critical. Once the infrastructure of transmission lines are in place, the renewable power plants are far more cost effective than any other options. Both wind and solar are now less than $.02 per KW, and the combined wind-solar is coming in at less than $.03. Such new power can come onboard in months, not years or decades required for other types of power.

Still, the problem is smoothing out the power for night time when the wind is not blowing. Thus the reliance on storage if we are to move to total renewables. If – well, when – the combined renewable energy and storage costs are lower than coal, oil and natgas, there will be no need for fossil fuels, except maybe for those places where the sun doesn’t shine (much) and the wind doesn’t blow (much).

There are many different options for storage of energy.

Fixed storage can be in the form of solar that moves water (back upstream to a dam that is above the existing hydro power system). It can use mirrors to focus heat for molten salt, for example.

The old lead battery technology has been tried and proved for a century and still is alive and well in the golf-carts.

Many players are after the battery storage market. GE is fighting hard against Tesla (powerwall battery built in their GigaFactories for fixed and battery packs for their cars) and Siemens. Storage options that are as good, or better, then lithium are coming fast to market for different applications. See a great view of new battery technologies in Pocket Lint. Batteries technologies that contain more carbon, nickel or cobalt seem very intriguing. Hydrogen options using fuel cell has been right at the edge of mass breakthrough into the market for decades.

When will certain storage options become a game-changer for existing “built economy” such as fossil fuels?

At some point, the combined renewable and storage will be sufficiently powerful and affordable to render the old fossil fuel options obsolete. McKinsey report discusses this massive drop in price and trend in their battery report. In 2010 battery storage cost about $1,000 per kilowatt hour of storage; their June 2017 report shows it at $230 per kwh in 2016 and dropping fast. It should be well below $200 per kwh now. (Batteries for the Telsa Model 3 are supposed to be at about $190 per kWh based on mass manufacturing; estimates based on SEC filings are for $157 kWh by 2020.)

So, what is the break-even point where storage becomes the game changer, and renewables with battery deflect the entire energy industry onto another course? Apparently, $125 per kWh is the disruptive price point. A scientist name Cadenza has developed battery technology at this price point using super cell and is now working on an extended version that includes the peripherals with the battery at, or below, the magical $125 kWh. She must demonstrate both cheaper and safer, so the housing is critical to avoid fires and short-circuits. “In March of this year, Cadenza published its report (pdf) saying that its super-cell technology can indeed hit that point.”

The technology is already here, yet new improvements are leap-frogging each competing option. How long before fossil fuels are an obsolete option? For just plain generation, fossils are dead and dying. Combined is where the war is won, however.

We argue that you really want to be careful with your oil and gas investments because you can find yourselves, like the oil patch (countries and companies and refiners) with stranded assets.

Moore’s law is at work in the battery complex. How long before combined renewables with storage supplants fossil fuels? Five years? Ten? Twenty?

Scenarios of Stranded Assets in the Oil Patch

The researchers over at Strategic Business Planning Company have been contemplating scenarios that lead to the demise of oil. The first part of the scenario is beyond obvious. Oil (and coal) are non-renewable resources; they are not sustainable; burning fossil fuels will stop — eventually. It might cease ungracefully, and here are a few driving forces that suggest the cessation of oil could come sooner, not later. Stated differently, if you owned land that is valued based on carbon deposits, or if you owned oil stocks those assets could start to become worth less (or even worthless).

We won’t spend time on the global warming scenario and possible ramifications of government regulation and/or corporate climate change efforts. These could/would accelerate the change to renewables. There are other drivers away from fossil fuels including: National Security, Moore’s Law toward renewables; and, efficiency.

1. National Security. Think about all the terrorist groups and rogue countries. All of them get part, or all of their funding from oil (and to a lesser extent, NatGas and Coal). Russia. Iran. Lebanon, where the Russians have been enjoying the trouble they perpetuate. The rogue factions in Nigeria. Venezuela. Even Saudi is not really are best friend (15 of the 19 bombers on 911 were Saudi citizens). Imagine if the world could get off of fossil fuels. Imagine all the money that would be saved, by not having to defend one countries aggression on another if the valuable oil became irrelevant. Imagine how much everyone would save on military. This is more than possible with the current technology; but with Moore’s law of continuous improvement, it becomes even more so.

2. Moore’s Law. Moore’s law became the law of the land during the computer chip world, where technology is doubling every 18 months, and costs are reducing by half.  (See our blog on The Future of Computing is Taking on a Life of Its Own. After all these decades Moore’s law is finally hitting a wall.) In the renewable world, the price of solar is dropping dramatically, when the efficiency continues to increase. For example the increase of 30% on imported PV, matches the cost reductions of the last year. In the meanwhile battery efficiency is improving dramatically, year-over-year. Entire solar farms have been bid (and built) for about $.02 per kilowatt and wind and/or solar with battery backup is about $.03 per kilowatt. At that price, it is far cheaper to install renewable power vs coal or NatGas, especially given the years to create/develop for fossil fuel plants.

Note, that we haven’t even talked about peak coal and peak oil. Those concepts are alive and well, just that fracking technology has pushed them back maybe 10 years from a production supply-side perspective. At some point you hit the maximum possible production (on a non-renewable resource) and production can only go down (and prices go up) from there. The world production of oil is now up to 100m barrels per day.  But oil wells deplete at about 4%-5%, so you need 4% more new wells every year. Fracking drops about 25%-30% in the first year! So you need about many more wells each year to stay even. But let’s go on to efficiency and probably the major demand-side force.

3. Efficiency. The incandescent light bulb, produces very little light… it produces more than 95% heat, and just a tiny bit of light with 100 watts of energy. With only 10-15 watts an LED light can produce the same light was required 100 watts in days of old. The internal combustion engine is hugely inefficient, producing mostly (unused) heat and directly harnessing only 10-15% of energy from gas or diesel… plus it took huge amounts of energy to mine, transport, refine, transport, and retail the fuel. Electric engines are far more efficient, and they produce no toxic emissions. A great book that talks about energy, efficiency and trends is by Ayers & Ayers, Crossing the Energy Divide. The monster power plants (nuclear, coal, NatGas) have serious efficiency issues. They produce huge amounts of heat for steam turbines, but most of the heat is lost/wasted (lets say 50%). Electricity must be transmitted long distances through transmission lines (where up to 40% can be lost in transmission).

Producing power as needed, where needed, makes so much more sense in most cases. Right now, using today’s technology, pretty much everyone can produce most of their own power (PV or wind) at about the same cost as the power monopolies.  But Moore’s law is making the renewable technology better and better every year. Add some batteries and microgrid technology and you have robust electric systems.

The losers in these trends/scenarios can be the BIG oil companies and the electric monopolies. They will fight move until they change, or they lose. Just like peak oil, it is a mater of time… but the time is coming faster and faster…

Saudi is trying to keep prices high enough to complete their oil Initial Public Offering so they can diversify out of oil. Venezuela is offering a new cyber coin IPO (their Petro ICO) with barrels of buried oil as collateral (See Initial Kleptocurrency Offering). But what if that oil becomes a stranded asset? Your Petro currency becomes as worthless as the Venezuelan Bolivar.

You really want to carefully consider how much and how long you want to own fossil fuel assets… Fossil fuels may be dead in a decade or two… Moore or less.

Qubit

The Future of Computing Is Taking on a Life of Its Own

Previously, we talked about the Tic-Toc of computing at Intel, and how Gordan’s law (Moore’s law) of computing – 18 months to double speed (and halve price) – is starting to hit a brick wall (Outa Time, the tic-toc of Intel and modern computing). Breaking through 14 nanometer barrier is a physical limitation inherent in silicon chips that will be hard to surpass. Ed Jordan’s dissertation addressed this limit and his Delphi study showed what the next technology might likely be, and how soon it might be viable. His study found that several technologies were looming on the horizon (likely less than 50 years)… and that organic (i.e. proteins) was the most promising, and should certainly happen sometime in the next 30 years.

Apparently quantum computing technology is here and now– kinda – especially at Google. See Nicas (2017) WSJ article about Quantum computing in the Future of Computing. As the article states about the expert Nevens, he’s pretty certain that no one understands quantum physics. At the atomic level, a qubit can be both on and off, at the same time. The conversation goes into parallel universes and such… Both here and there, simultaneously. The Quantum computer is run in zero gravity, at absolute zero temperature (give or take a fraction of a degree). Storage density using qubits is unimaginable. The computer works completely differently, however, based on elimination of the non-feasible to arrive at good answers, but not necessarily the best answer. Heuristics, kinda. The error rate is humongous, apparently, requiring maybe 100 qubits in error correction associated with a single working qubit.

Ed Jordan was reminiscing about quantum computing yesterday… “Basically, all computing in all its permutations need to be rethunk. Quantum computing is sort of the Holy Grail. One could argue it is sort of like control fusion: always just 10 years away. Ten years ago, it was 10 years away. Ten years from now it may still be ten years away. There is a truck load of money being thrown at it. But there isn’t anything mature enough yet to do anything that looks like real computing. The problem is how do you read out the results? Like Schrödinger’s cat, that qubit could be alive or dead, and by looking at it you cause different results to happen – as opposed to something that exists independent of your observation.”

Quantum computing is now moving past the technically impossible into the proved and functional, and maybe soon to be viable. The players in this market are Google (Alphabet), IBM and apparently the NSA (if whistle blower Snowden is to be believed.)

Intel may not be able to capitalize on the next generation of computing.  Some computations, such as breaking encryption, can probably be done in a couple seconds on a quantum computer, even though it might take multiple current silicone computers a lifetime. There are several potential uses of the quantum computer that make businesses and security targets very nervous.

Jordan and Hall (2016) talk about using Delphi to anticipate deflection points that are possible on the horizon, including those scenarios that would be possible via quantum computing, or bio-computing for that matter. The use of experts or informed people could make the search for such deflection points more evident, and the ability to develop contingency plans more effective.

One of the most interesting things in the Nicas article is a look at the breakthroughs in computing technology, and comparing them to Jordan’s 2010 dissertation. He found that two or three types of technology should likely be feasible within 25 to 40 years and viable in application within about 30 to 50 years. In his case that would be as early as about 2040. Note that the experts discussed by Nicas were pegged to have full application of a quantum computer by about 2026; that is when digital security will take on a whole new level of risk. It also makes you wonder how block-chain (bitcoin) will fare in the new age of supersonic computing.

This seems like a great time to start working of security safeguards that are not anything like the current technology? Can you imagine the return of no-tech or lo-tech? Kinda reminds you of the revival of the old “brick” phones for analog service (in the middle of the everglades).

References

Debnath, S., Linke, N. M., Figgatt, C., Landsman, K. A., Wright, K., & Monroe, C. (2016). Demonstration of a small programmable quantum computer with atomic qubits. Nature, 536(7614), 63–66. doi:10.1038/nature18648

Jordan, Edgar A. (2010). The semiconductor industry and emerging technologies: A study using a modified Delphi Method. (Doctoral Dissertation). Available from ProQuest dissertations and Theses database. (UMI No. 3442759)

Jordan, E. A., & Hall, E. B. (2016). Group decision making and Integrated Product Teams: An alternative approach using Delphi.  In C. A. Lentz (Ed.), The refractive thinker: Vol. 10. Effective business strategies for the defense sector. (pp. 1-20) Las Vegas, NV: The Refractive Thinker® Press. ISBN #: 978-0-9840054-5-1. Retrieved from: http://refractivethinker.com/chapters/rt-vol-x-ch-1-defense-sector-procurement-planning-a-delphi-augmented-approach-to-group-decision-making/

Nicas, Jack (2017, November/December). Welcome to the quantum age. The future of Computing in Wall Street Journal. Retrieved from: https://www.wsj.com/articles/how-googles-quantum-computer-could-change-the-world-1508158847

Outa Time, the tic-toc of Intel and modern computing.

Ed Jordan’s dissertation research looked at the future of computing. He was inspired by the thought that Gordon’s law (Moore’s law) of computing — 18 months to double speed (and halve price) — was about to break down because of the limitations of silicon chips as the go below the 14 manometer level. Since Intel lives and dies based on the silicon chip, his research was really a story into the future. When will the old chip die, and what will be the next technology?

Hall & Jordon discuss the application of this disruptive technology in their DoD procurement planning article in the Refractive Thinker related to the use of Integrated Product Teams.

His research showed that the death of the silicon chip computer would come sooner, not later. And that several options appeared likely including quantum computing.  Scientists have just made a huge breakthrough toward Quantum Computing: see the WSJ article about it here, as published in the journal Nature.

In the meantime, Intel’s approach for decades of hardware one year and software (for the new hardware) the next has broken down. The so-called Tic-Toc of Intel is now outa time. It seems to be more like 2 years (4 years, really) in the clock cycle.

So, will Intel die with the new technologies? Obviously Intel can simply invent the disruptive technologies internally, or buy it up wherever the viable invention wells up.

References

Debnath, S., Linke, N. M., Figgatt, C., Landsman, K. A., Wright, K., & Monroe, C. (2016). Demonstration of a small programmable quantum computer with atomic qubits. Nature, 536(7614), 63–66. doi:10.1038/nature18648

Jordan, Edgar A. (2010). The semiconductor industry and emerging technologies: A study using a modified Delphi Method. (Doctoral Dissertation). Available from ProQuest dissertations and Theses database. (UMI No. 3442759)

Jordan, E. A., & Hall, E. B. (2016). Group decision making and Integrated Product Teams: An alternative approach using Delphi.  In C. A. Lentz (Ed.), The refractive thinker: Vol. 10. Effective business strategies for the defense sector. (pp. 1-20) Las Vegas, NV: The Refractive Thinker® Press. ISBN #: 978-0-9840054-5-1. Retrieved from: http://refractivethinker.com/chapters/rt-vol-x-ch-1-defense-sector-procurement-planning-a-delphi-augmented-approach-to-group-decision-making/

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