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Coinbase SEC Filing Analysis: Revenue Doubles, Expenses Up

Yogi Nelson

Namaste Yogis. Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered! Here no question is too mundane. As a bonus, a proverb is also included. Today’s question was submitted by Victor from San Juan, and he wants to know my view of the crypto exchange company Coinbase.

Victor, you came to the right place. It just so happens, that I’m a Coinbase customer and own their stock hence I try to keep up on their performance. Let’s go through their August 7th, Securities and Exchange Commission (SEC) filing, to answer your question. We start by comparing a few important data points from December 31, 2023, to June 30, 2024.

At the end of 2023, Coinbase had $5.13B in cash; whereas on June 30th, 2024, it had $7.22B. Two billion dollar plus increase in six months is noteworthy. Coinbase provides crypto custody services, therefore let’s examine that metric. It turns out Coinbase now is the custodian of $262B in crypto assets whereas on December 31st, it had only $193B in customer accounts. A $70B increase is monumental. I suspect this increase is tied to the Bitcoin ETF approved by the SEC in January. Coinbase was selected by Blackrock and other investment managers as their Bitcoin ETF custodian. Holy vault Batman!

The amount of crypto held for investment by Coinbase climbed from $330M to $1.23B! Coinbase stockholders were delighted with their equity stake growing to $8.37B from $6.28B in only six months. Last, revenue more than doubled year over year to $1.38B but the net income was not particularly impressive, checking in at only $36M. Over 50% of Coinbase’s revenue is generated by fees placed on trading. Hence, that is a concern.

What about expenses? Short answer, expenses are up across the board. For example, transaction expenses, marketing, technology development, and general administrative all increased 10% plus. In an earlier article, I noted Coinbase pledge to spend millions on supporting pro-crypto politicians in this election cycle. Those expenses will shoot up during the third and fourth quarter as the general election approaches in November. Will there be a return on investment of those political contributions? Maybe, but it will take years provided the election goes the way Coinbase seeks. If anti-crypto candidates win, Coinbase could find itself with fewer friends and more opponents.

From a macro perspective there is good news for Coinbase’s business model. For instance, in the centralized crypto exchange world, Coinbase has no serious competitor. Most people prefer to participate in crypto via a centralize exchange and Coinbase reigns supreme for several reasons. First, Coinbase has an easy-to-use interface. Super simple to navigate, on-board, etc. Second, Coinbase is USA headquartered and registered with the SEC thus clients perceive there is a “cop on the beat”. Third, Coinbase takes extraordinary steps to ensure compliance with SEC rules which is exactly why Blackrock and other institutional investors selected Coinbase as their Bitcoin ETF custodian. Coinbase basically has the lion share of the centralized exchange and custodial market except for a small percentage of people who insist on peer-to-peer trades and being their own custodians. Could Coinbase lose market share in the future? Yes, a new centralized exchange might challenge. However, Coinbase is unlikely to lose significant clients due to the mass market going the way of peer-to-peer exchange and custodian free.

Time to go, but first a proverb from Vietnam, where they say: “talking to you is like pouring water over a taro leaf” (in one ear out the other).

Until next time,

Yogi Nelson

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WHAT IS THE CONNECTION BETWEEN MORGAN STATE UNIVERSITY AND XRP RIPPLE?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Hiram from Los Angeles, and he wants to know, what is the connection between Morgan State University and XRP Ripple (XRP)?

Hiram, you came to the right place.  Not long ago, I too was wondering about the connection between universities who cater to people of color and blockchain technology.  Your question prompted me to investigate the issue.  I’ll share my discovery and the fascinating connection between Morgan State University (MSU) and XRP.  Let’s commence with a short description of Morgan State University and XRP.

The MSU campus is in Baltimore, Maryland.  MSU was founded in 1867 as a public university dedicated to higher education of African Americans during a time in American history when Black people could not enter “main-stream” universities due to the rampant and institutional racism of the USA.  Today, MSU offers bachelors, masters, and doctorate degrees across dozens of fields, including computer science.  MSU of course fits into the category of Historically Black College and University (HBCU). The MSU campus was declared a national treasure by the National Trust for Historic Preservation!  Holy precious jewel, Batman!  What about XRP, you ask?

XRP is a San Francisco based blockchain and crypto currency company specializing in efficient and low-cost international money transfers.   Unlike Bitcoin, XRP uses a proof of stake consensus and a network of validators, making transactions faster and more energy efficient. XRP established the University Research Blockchain Initiative (URBI) in 2018 with the intent of fostering innovation for the next generation of blockchain engineers at Historically Black Colleges and Universities.  A blockchain with a heart, Batman!

Partnering with universities is not new to XRP.  In fact, XRP has 50+ university partners spread across 26 different countries, has developed the curriculum for 600+ fintech courses, and sponsored over 1,500 research projects.  Essentially, the foundation arm of XRP, known as URBI as noted above, deliberately focuses on developing international software engineering talent.  This makes perfect sense given XRP is in the international money transfer business. 

Let’s now discuss the nature of their partnership. Initiated in 2018, the original collaboration between MSU and XRP bolstered technological bandwidth, set new academic excellence standards, and cultivated a culture of innovation. The partnership gave rise to innovative programming such as the student-led “Morgan-Hack-A-Thon”, which drew about 150 students from 7 HBCUs and featured an XRP Ledger workshop and Finance Challenge Statement from XRP.  Under the terms of the renewed three-year partnership, XRP will donate another $1.05M to MSU.  The additional funding will lead to the creation of opportunities for new programming and research for students and faculty, says XRP.   Holy renewal of vows, Batman! 

From the perspective of MSU, they aim to boost on-going efforts, while also engaging other HBCUs, says, Ali Emdad, MSU Business School Dean. MSU also notes the importance of combining collaborative research with real-world applied learning opportunities, particularly in the fin-tech space.  Ultimately, the goal is to nurture a pipeline of diverse, skilled, and highly trained professionals ready to excel in enterprise blockchain jobs.  The partnership envisions activities that include cutting-edge research endeavors, faculty-led projects intersecting blockchain, artificial intelligence (AI) and machine learning (ML), XRPL development workshops, and an annual national blockchain and fintech conference.  Holy practical partnership, Batman!

I end with this proverb from Belize, where they say: Monkey knows from which limb to swing!

Until next time,

Yogi Nelson

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ARTIFICIAL INTELLIGENCE AND AUTOMOBILES, WHAT’S THE LATEST?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Stelios from California and he wants to know what is happening with artificial intelligence (AI) in automobiles?

Stelios, you came to the right place.  After you asked me this question, I decided to do a little research and found an informative article from PwC.  I’ll start with a word about PwC and thereafter dive into their findings. 

Who is PwC?  PwC is one of the Big 4 accounting firms in America.  PwC is also a major international firm, with offices across the globe.  In addition to accounting services, PwC offers business consulting, market intelligence, and strategic development.  Recently PwC produced a report titled, Is GenAI a game changer for the e-mobility ecosystem? 

https://www.pwc.com/m1/en/publications/is-gen-ai-a-game-changer-for-the-emobility-ecosystem.html

Okay, now it’s time to examine the fundamental premise from where PwC begins.

Anyone who has purchased a new car recently knows automobiles are increasingly more electronic and software driven than mechanical.  The trend started 10 – 15 years ago and has accelerated quickly.  PwC says, software and electric vehicles will be the largest value pool of cars by 2030!

With that as a baseline premise, PwC explored the following question “… how can Generative AI (GenAI) catalyze a transformative revolution in the electric mobility sector leveraging its novel capabilities to build predictive models, develop bespoke services, optimize processes across the value chain and much more”.  Holy forecasting insight Batman! Let’s check out the six predictions in greater detail below.   

Research and Development (R&D).  PwC asserts AI will accelerate research and development (R&D) of new materials and testing processes and thereby reduce production of prototype timelines drastically.  PwC envisions AI will analyze a wide range of potential battery chemistries, for instance.

Generative Design: PwC reports that by using GenAI to analyze the significant increase in data, more design possibilities are accessible.  GenAI enable the design process leading to innovations in and optimized vehicles. 

Demand Forecasting and Inventory Optimization:  Essentially, the argument PwC makes here is that with GenAI automobile manufacturers will be able to forecast with greater precision and thus reduce waste.  For example, the GenAI system will identify demand patterns, seasonality, and potential fluctuations.    

In-Car Personalization:  This one could be particularly cool if done right.  PwC says that with GenAI user preferences, historical user interactions, sensor data, etc., could be used to customize the driving experience to the vehicle owner. We need this for the Batmobile, Batman!

Intelligence Energy Management Systems:  Clearly an electric vehicle is energy efficient, that we know.  However, the point PwC makes goes beyond a particular car and includes the entire production chain.  Wow, I hadn’t considered that.  Good point.

Traffic Control and Optimization:  With GenAI says PwC, traffic patterns, route optimization, and of course congestion management. 

Although AI in cars has existed for 10+ years, PwC concludes with the assertion that GenAI technology will hit the mass market by 2030 and become the dominant technology.  I don’t think there is any reason to disagree. What say you, Stelios?

I conclude with a proverb from Chile, where they say: liar falls faster than a thief. 

Until next time,

Yogi Nelson

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SHOULD YOU BE WORRIED IF YOU LAWYER USES ARTIFICIAL INTELLIGENCE (AI)

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Esquire Nave and he wants to know if he should be worried if his lawyer uses artificial?

Attorney Nava, you came to the right place.  Great question I found an interesting article on this subject, titled AI on Trial, Legal Models Hallucinate in 1 out of 6 Queries. https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-queries. The report was produced by Stanford’s Human AI Center.  Let’s examine their findings and the implications for the legal profession, beginning with basic findings of fact.

Large language models such as ChatGPT, are not ready for prime time in the courthouse, is the headline.  Stanford’s HAI Center study reports error rates were as high as 69%.  Holy inadmissible evidence Batman! U.S. Supreme Court Chief Justice felt compelled to make a point regarding this problem in his 2023 Annual Report on the Judiciary https://www.supremecourt.gov/publicinfo/year-end/2023year-endreport.pdf

Is there a solution?  People working on retrieval augmented generation (RAG) say yes, and they have it.  According to RAG advocates, RAG reduces hallucinations in domain-specific contexts. RAG proponents claim they can avoid hallucinations and can guarantee legal citations.  Essentially, RAG systems promise to deliver more accurate and trustworthy legal information by integrating a language model with a database of legal documents. Can RAG advocates deliver on these bold claims?  Not yet says, Stanford’s HAI Center.

Stanford put two RAG providers to the test, Lexus-Nexus and Thomson-Reuters.  Stanford found RAG providers are less prone to hallucinations when compared to ChatGPT, etc.  However, given that non-RPG models produced an error rate of 69% that wasn’t much of an accomplishment.  According to Stanford, RAG models hallucinated 17% (1 of 6) when prompted.  Which brings us to the next question, what types of queries were asked of the RAG models.  Answer, there were four basic types.

The Stanford HAI Center says they prompted the models with the following four types of queries:

1) general research questions (questions about doctrine, case holdings, or the bar exam);

2) jurisdiction or time-specific questions (questions about circuit splits and recent changes in the law);

3) false premise questions (questions that mimic a user having a mistaken understanding of the law); and

4) factual recall questions (questions about simple, objective facts that require no legal interpretation). 

The RAG models fell into one of two types of hallucination errors.

The first was to describe the law incorrectly or make a straight factual error.  The second type of error was mis-grounded.  When mis-grounded errors were produced, the AI model describes the law correctly, but cited source which did not support its claims.  Either way, a problem; a big problem.  Mis-grounded hallucinations might be more troublesome because they undermine the point of AI in legal research; namely, AI reduces the time required to conduct research.

Stanford identified three challenges facing RAG model developers.  First, legal retrieval is difficult because the “law” is more than verifiable facts; it also consists of rulings and interpretations.  Second, the complexity of American jurisprudence means retrieval of inappropriate authoritative documents happens often. Last, sycophancy is a concern.  Sycophancy is the tendency of RAG models to agree with the user’s incorrect assumptions. 

Bottom line–RAG models are superior to ChatGPT but require greater transparency, refinement, and not yet ready for Broadway.

We end with a proverb from Estonia:  One fool can ask more questions than 10 men can answer.

Until next time,

Yogi Nelson

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WHAT TOOLS ARE BEING CREATED TO ENSURE ARTIFICIAL INTELLIGENCE SECURITY?

Yogi Nelson

Namaste Yogis.   Welcome to the Blockchain & AI Forum, where your blockchain and artificial intelligence technology questions are answered!   Here no question is too mundane.  As a bonus, a proverb is also included.  Today’s question, was submitted by Ish and he wants to know what tools are being created to ensure artificial intelligence (AI) security?

Ish, you came to the right place.  I did a little research and discovered an excellent report from the RAND Corporation.  Let’s review what they say, but first a word about the Rand Corporation.

The RAND Corporation is a non-profit policy analysis organization.  RAND was established in 1948.  Their mission is to advise governments and large corporation on matters of national importance.  RAND was contracted by the United Kingdom to examine the range of tools designed for the development, deployment and use of trustworthy AI in the United Kingdom and the United States.  Wow!  No easy task, Batman!  What did RAND conclude?

Let’s begin with tools for trustworthy AI.  RAND acknowledges the rather obvious point—there is not a universally accept definition for the term “tools for trustworthy AI”.  However, governments and large institutions have consistently articulated certain principles that would enhance trustworthiness in AI.  Those principles include fairness, transparency, accountability, privacy, safety and explainability.  Notwithstanding the lack of consensus, RAND made the attempt and offered the following definition:

Tools for trustworthy AI are specific approaches or methods to help make AI more trustworthy and can help to bridge the gap between the high-level AI principles and characteristics, on the one hand, and the practical implementation of trustworthy AI, on the other.

Based on their definition, RAND identified far more tools for developing trustworthy AI than what I expected—233 to be precise.  RAND divides the tool set into three categories:  technical, procedural, and educational.  You may be surprised to learn the US depends more on technical professionals and academia to develop AI trust tools when compared to the UK.  Additionally, large US corporation develop more trustworthy AI tools compared to their counterparts in the UK.  The UK prefers procedural methosds and relies less on academia. 

As expected, the RAND study offers a series of recommendations.  RAND offers the following guiding principle:

AI tools need to be complemented by a collaborative and inclusive approach that involves multiple perspectives and actors, such as governments, businesses, civil society, academia and international organizations.  In other words, be willing to listen to voices from across the spectrum.  RAND also suggested governments use the following methodology:

  1.  Link up with relevant stakeholders to proactively track and analyze the landscape of tools for trustworthy AI in the UK, the US and beyond.
  2. Systematically capture experiences and lessons learnt on tools for trustworthy AI, share those insights with stakeholders and use them to anticipate potential future directions.
  3. Promote the consistent use of a common vocabulary for trustworthy AI among stakeholders in the UK and the US.
  4. Encourage the inclusion of assessment processes in the development and use of tools for trustworthy AI to gain a better understanding of their effectiveness.
  5. Continue to partner and build diverse coalitions with international organizations and initiatives, and to promote interoperable tools for trustworthy AI.
  6. Join forces to provide resources such as data and computing power to support and democratize the development of tools for trustworthy AI.

Time to end with a proverb from the UK where they say: Every cloud has its silver lining.

Until next time,

Yogi Nelson