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Cambyses' Industry Initiatives

The articles on this page are an outgrowth of Cambyses Financial Advisor’s Industry Initiatives. Our Initiatives cover immature, high risk, or speculative industry segments we believe provide future business opportunities but are not yet, with some exceptions, suitable for many investors. Our initiatives, their coverage, and our starting dates for them include:

Artificial Intelligence - AI (2016): General and Generative AI, Machine Learning, Large Language Models, Mathematical Optimization and Regression, Cloud and Edge Computing, Robotics and Automation, Metaverse, and System Security. (Theory, Software, Hardware, and Social Impacts)

Electric and Autonomous Vehicles - E&AV (2016): All Classes of Electric and Autonomous Vehicle Manufacture (Ground, Sea, or Air), Sensors, Semiconductors, Charging and Grid Issues, Batteries, Raw Materials (Lithium and Rare Earths), Fuel Cells and Hydrogen Systems, and End Users.

High Energy Physics - HEP (2018); Fission and Fusion Energy Systems, Laser Technology, and Solar Phenomena (With an occasional Alt-Energy topic, just for variety.) 

Quantum Computing - QC (2017); Quantum Hardware, Software, Quantum Superiority and System Security, Super Conduction, 

Commercial Space Ventures (2017); Launch Vehicles and Systems, Satellites, Crewed Flight, and extended Orbital Operations (e.g. Parker Solar Probe)

As of Late August 2023, several of the industries we follow in our Initiatives have become relatively mainstream. For example. Artificial Intelligence, Electric Vehicles, and Commercial Space Ventures no longer seem as esoteric as they did six to eight years ago.  (Quantum Computing and High Energy Physics, on the other hand....) Convergence has blurred the distinction between, for example, AI and all of the other Initiatives.

Notwithstanding these changes, we intend to keep chronicling these industries and technologies until sentient quantum-AI's  riding autonomous nuclear powered vehicles across the surface of Jupiter's moons become an everyday event.

Quantum Computing - Overview
Electric Vehicle Charging

The Quantum Market and Its Potential

Quantum Computing (QC) is in its infancy but may grow up fast. The current market for quantum computing is small - approximately $2.5 billion annual revenue. Today's commercial quantum products and services center around [*1]


  • Shared quantum hardware,

  • Cloud and AI accelerators (for Machine Learning and Large Language Models),

  • Optimization and supply chains,

  • Simulation (molecular biology and pharmacology), and

  • Cryptography and Cybersecurity

Commercial funding for quantum computing is heavily subsidized. Government funding from China, the United States, and the European Union, much of it related to cryptography, cybersecurity, and optimization [*2], reached $23.5 Billion in 2022.

Analysts believe QC's market will grow quickly.- a Cumulative Average Growth Rate near 31% over the next five years, and an eventual $1.0 trillion market. Whether that translates to investment opportunity probably depends on timing.

If QC follows the path several other high-tech, high-capital markets have followed: a lengthy period of slow price appreciation gives way to a rapid (and largely irrational) bubble (up and down) followed by a lengthy recovery that reflects rational investment assumptions. [*3]


The Quantum Computing investment-ecosystem encompasses academic and government institutions, literally dozens of privately held companies, and a number of public companies, thus, affording a variety of portfolio allocation options.


Diversification options among the pubic companies are limited. Virtually all the public companies occupy the Technical or Information sectors - primarily computer hardware, software, components (semi-conductors), data services (Cloud and AI), and mission-critical support services.


Don't hold your breath or save your allowance for consumer versions of quantum machines. Current and foreseeable QC-tech is too expensive, complex, bulky, and balky for home applications. Instead, we expect some variation on Quantum as a Service (QaaS) to emerge in the consumer market. Commercial offerings along similar lines have already entered the market.


The Quantum Ecosystem and Players

Publicly traded companies in the quantum ecosystem provide a range of services and products that mirrors, with some stark exceptions, the standard computer economy:

Full Spectrum Quantum Products and Services

  • The primary players in the full spectrum market {Amazon (AMZN), Apple (AAPL), Alibaba (BABA), International Business Machines (IBM), Alphabet (GOOGL), and Microsoft (MSFT)} provide a range of quantum services as part of their broad product offer. These companies lead the quantum industry, but are not quantum centric. For example, each of them has launched cloud services based on quantum platforms that supplement their existing cloud offer. They are the safest options for investors who want a presence in the segment, but want to avoid many of the major risks.

  • While Amazon and Friends play for headlines, Honeywell (HON) subsidiary Quantinuum has quietly become the largest globally integrated quantum computing entity. 

  • D-Wave Quantum (QBTS) has the distinction of being the only full spectrum and quantum centric company in Cambyses' Quantum initiative. Their product/service offer is solely based on quantum, to the exclusion of "computer classics."



  • Standalone quantum-software developers are numerous. They constitute roughly half of Cambyses Initiative's fifty-two companies.

  • Many of these companies suffer a bad case of the "Toos." They are too volatile. They are making too little money (or losing too much). Their price is too low. (Over-valued penny stocks) And finally, they are too dependent on equity infusions. (Low sales volume and minimal cash from operations) Thus, while Cambyses follows and studies these companies, we consider most of them unsuitable for our investors.

  • Quantum Computing, Inc. (QUBT), a provider of quantum based finance, healthcare, and supply chain management software is archetypical.


Data Security

  • Depending on who and when you ask, quantum computing is either the best thing that ever happened to data security and privacy or the final death knell for both.

  • Both arguments are readily supportable. Several quantum computing capabilities [*4], give rise to new, and virtually unbreakable, data encryption/exfiltration techniques. At the same time, those characteristics threaten to overwhelm existing data defenses. The U.S. Government Accountability Office and the European Data Protection Supervisor consider the latter possibility so acute that they have sponsored software development "competitions" that emphasize solutions to the encryption issue [*5].

  • Arqit Quantum (ARQQ) and Quantum Corporation (QMCO) are developing data security responses tailored to both structured and unstructured quantum data.



  • For most of the last 25 years, IonQ (IONQ), a trapped-ion quantum computer developer, was the sole stand-alone quantum computer manufacturer.

  • Rigetti Computing's (RGTI) 2021 IPO added an additional market participant.

  • With the exception of these two companies, government, academic, or "Full Spectrum" companies have developed most quantum computer platforms.


Semiconductors and Superconductors

  • The list of quantum semiconductor suppliers reads like The Usual Suspects: Nvidia (NVDA) and Intel (INTC) lead the development effort, followed by Advanced Microdevices (AMD) in a manufacturing role.

  • Literally all of the current quantum computing paradigms support their Quantum Processing Unit (QPU) with low-temperature superconductor technology, In-house development - with each of the full-spectrum, hardware, and semiconductor manufacturers developing their own proprietary approach - accounts for most of the progress to date. Cambyses is not convinced that is the most efficient development route.

ETFs and Mutual Funds

  • We are aware of only one quantum-focused ETF, Defiance Quantum ETF (QTUM). We note, however, that most (if not all) NASDAQ and Large Cap funds include the large-cap companies we include in our "Full Spectrum," "Hardware," and "Semiconductor-Superconductor" lists.


[*1] The list implies that quantum computer capabilities are best applied to multi-variate, multi-objective problems that take advantage of QC's massive scalability and parallelism. That impression may be more attributable to our lack of imagination than it is to QC's capabilities. As the technology matures, additional paradigms may emerge.

[*2] DARPA, for example, has supported optimization, data security, and encryption studies. Like many things involving DARPA, this support has both a bright and a dark side. Optimization, for example, can be applied to assure a steady supply of needed product at least cost. With minimal changes to the algorithm and a different set of environmental sensors, it can be weaponized to facilitate target identification, selection, prioritization, and execution. The virtues of the latter scenario depend on what is being targeted, by whom, and why.

[*3] See for example, the dot com bubble, the most recent 5-8 years of electric vehicle stock history, and where we at Cambyses believe the AI securities market is headed.


[*4] Quantum computers are particularly suited to vector-tensor analysis, and, by extension, to prime factorization. QCs are so good at prime factoring that one leading researcher quipped; "So far, we have spent about two billion dollars to prove that 15 = 3 x 5." Since virtually all public/private key encryption and RSA algorithms rely on prime factorization, the algorithms and the data they protect are extremely vulnerable to attack by quantum systems. The speed at which quantum computers can perform these calculations allows communications and stored data to be compromised in real time, thus weaponizing quantum computers.


[*5] These "quantum proofing" exercises have been less than stunningly successful. In one notorious instance, a "quantum-proof" encryption algorithm (certified as such by the U.S. Government Accountability Office) was "hacked and broken" in under one hour using an off the shelf $500 laptop, without quantum assistance.

State of the Industry - September 2023

As the EV industry evolves, charging infrastructure, its performance, or availability has emerged as the primary focus of customer dissatisfaction with both the infrastructure and EVs themselves.


Customer dissatisfaction starts with the vehicles themselves: As with Internal Combustion Engine autos, most EV driving activity takes place within 100 miles of home. An EV round trip within that zone presents few anxieties. EVs currently in the market have an average range of 211 miles. Half of the EV models have ranges greater than 300 miles, and two models have ranges greater than 400 miles. It is when EV owners leave their own neighborhood that their range-anxiety manifests.


Charger speed: Approximately 80% of the technology currently deployed is, simply, too slow. Level 2 chargers {AC, 208-240 Volt, delivered through a dedicated charging device} charge an EV to 80 percent from empty in 4-10 hours. That rate is acceptable only if you are content to limit your daily driving to 300-400 miles and/or have access to overnight charging facilities.


Until November 2022, Tesla’s DC charging platform (DC, 400-1000 Volt) was incompatible with other manufacturers plug-in connectors. This limited competitor access to Tesla’s high speed charging platform (Full charge in 20-60 minutes.) Tesla has since agreed to allow several manufacturers (Notably, Ford and GM), to use Tesla’s connectors – which, through “Musk Magic,” have now been dubbed the “North American Charging Standard (NACS).” Not to be outdone, a coalition of seven manufacturers [*1] recently agreed to deploy up to 30,000 fast charging installations that support both NACS and Combined Charging Standard connectors.


Charger Geography: The Department of Energy reports there are approximately 51,000 public charging stations in the U.S. (March 2023). The Petroleum Institute reports nearly three times as many gas stations are in operation. EV’s presently account for ~1.5% of vehicles, so this would seem a reasonable supply. Looking deeper, however, we find:

  1. EV charging stations are, predominantly, an urban phenomenon. Charging locations are concentrated in metropolitan areas – exacerbating the range issues in states and regions where population centers are far apart. As one writer observed, with an average EV range of 210 miles, a Nebraska Driver could not travel from Lincoln to North Platte in their EV.

  2. Charging Station distribution is heavily skewed. California has about 30% of commercial charging stations (14,040 of 51,000) – nearly four times as many as New York (the state with the second most stations). Alaska, Mississippi, and the Dakotas each have fewer than 100 stations. Based on population, the Plains States are a veritable desert of EV charging. [*2]


We don’t expect these patterns to abate in the near future.


Convenience, Availability, and Maintenance: Companies that invest in reliable hardware and robust maintenance practices earn EV users’ trust. Unfortunately, with the exception of Tesla and Volta, the major charging networks don’t seem to have gotten this message. A recent JD Power survey [*3] reports that twenty to thirty-five percent of EV drivers have arrived and departed from a charging station without gaining any range (charge) on their EV. Drivers report malfunctioning equipment, incompatibility, and lengthy delays for service as their primary sources of dissatisfaction.


This dissatisfaction is one factor driving manufacturers to internalize their charging infrastructure, or to align themselves with one or more of the emerging charging coalitions, This, in turn, impairs the future value of “pure-play” charging companies – a process reflected in both their revenue growth and their stock prices. It also portends a movement toward vertical integration and consolidation within the EV manufacturing industry itself. [*4]


Boredom: We admit we didn’t see this one coming – no matter how obvious it is in retrospect. Long charging times imply long periods sitting around and waiting. For the most part, there is very little to do while you wait.


This suggests there may be mixed-use-business opportunities that provide entertainment, recreation, food, or rest areas in conjunction with charging services. Perhaps, an enhanced version of gas-station mini-marts? The risk, for entertainment-recreation offerings, is that charging time improves to the point where there is no need for entertainment. This will probably drive most operators toward mini-mart and eating venue options. Just what the world needs – more fast food and strip malls.

[*1] BMW, General Motors, Honda, Hyundai, Kia, Mercedes-Benz,  and Stellantis

[*2] The Plains states are sparsely populated outside major urban areas and intermittent cold weather contributes to shorter battery life and slows EV adoption. The area’s staunchly Red politics and dependence on e.g., alcohol fuel revenue completes the picture.

[*3] We are hesitant to cite JD Power’s “trend” statistics due to the paucity of data on which they are based. Three data points, in our estimation, are not sufficient to establish or validate a “trend.”

[*4] We anticipated consolidation would first manifest in EV manufacturers’ supply chains. So far merger-consolidation (vertical integration) on the supply side has focused on batteries, semiconductors, and raw materials. Post-manufacture consolidation has emphasized dealer-distributorship and (most recently) charging infrastructure.

Artificial Intelligence - August 2023

NVIDIA Adds Depth and Breadth to its Artificial Intelligence Offer


NVIDIA (NVDA) positioned itself at the center of today's AI world with a new chipset that facilitates throughput for Machine Learning (ML) and Large Language Models (LLM) and the inferential training they require. Last week NVDA introduced the Grace Hopper Superchip, a CPU and GPU on a single platform that promises 7X the speed of the Intel et. al. PCIe 5.0 bus architecture, and 30X the bandwidth of NVDA's own DGX A100. 


Throughput and bandwidth identify-as the primary constraints on the Machine Learning and Large Language Models that are taking AI by storm this year. With 35-40% of companies already implementing AI in some form, much of it centered on massive databases and almost equally massive regression/optimization algorithms,[*1] data center overload threatens to become acute. NVDA was already near the top in the race to provide throughput. The Grace Hopper Superchip cements that industry leadership position - at least for the moment. 


NVDA is already the largest chip maker. Its $1.1 trillion market capitalization is roughly three times the market capital of its nearest rivals (Taiwan Semiconductor and AVGO-Broadcom) and eight times the size of industry household names, QualCom and Intel. Once seen as primarily a computer game facilitator and video editing solution, NVIDIA has become the premiere supplier of advanced GPUs and CPUs. The public and businesses IA fixation, centered on ML and LLM, make GPUs, potentially massive, parallelism and scalability, the technology of the hour - and NVDA its supplier. That combination has driven a nearly 220% appreciation in NVDA's stock price so far this year. Combine that with extraordinarily good fundamentals (Current ratio 3.4, Debt/Equity 0.4, Gross margin 56%) and it almost takes the terror out of NVDA's 228+ P/E. Indeed, 85% of analysts that we follow rate NVDA as a Buy or Strong Buy, and project stock appreciation averaging 16% for the coming year.[*2]


With all this going for it, we are still left with one burning question: Is the Grace Hopper Superchip actually smarter than its namesake? We find that difficult to imagine! If you haven't encountered legendary computer and programming pioneer Grace Hopper before, maybe it's time to find her Wiki? [*3]



[*1]  ChatGPT reportedly includes a 580 GB dataset and a 175 million parameter search/response algorithm. Approximately 10 million users access the software daily.

[*2] For comparison sake, CFA rates NVDA's risk as moderate or elevated (Four or Five on a 6-point scale), and 1 year share value growth as elevated (11-15%). We rank its performance (Return/Risk/Prospects) 62nd of the 246 securities we rank for our client's portfolios. Detrimental factors include high volatility (Beta = 1.8, standard deviation exceeding that of 99% of S&P securities), that extraordinary P/E (exposing NVDA to market valuation risk), and geopolitical factors that affect NVDA's sales in China.

[*3] Rear Admiral Grace Hopper is credited with, among many other things, initial concepts in linker-compiler architecture, contributions to higher level language programing (particularly COBOL), her advocacy of distributed networks, and one of the earliest mentions of a "computer bug" in the literature. Congress and Ronald Reagan extended Hopper's service well beyond the Navy's mandatory retirement age in order to preserve her contributions to the field. Hopper retired from the Navy in 1986 after 43 years in service. She continued to contribute to the field until her death in 1992 at age 85.

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