What History Says About MegaCap Companies with NVDA’s Multiples
What’s riskier, a stock like Nvidia that has been moving up since the start of the year and has now risen more than 200%, or the stock you pick by throwing a dart at the Wall Street Journal? Nvidia (NVDA) will report earnings on Wednesday, the report has a significant risk of disappointing, even if it exceeds forecasts.
Background
The last time Nvidia reported quarterly results, the chip maker forecasted record revenue that was far above anything it had accomplished before. Naturally, investors sent the stock up and then up some more to the level we see today. On Wednesday, August 23rd, the company will share the actual results of its second-quarter earnings. If it doesn’t deliver or exceed expectations, there may be a lot of disappointed investors. Plus, a repeat projection of future earnings growth would seem necessary to maintain its trajectory. Nvidia’s current valuation is extremely high, it has been the poster child of the AI investment boom, it would seem there are more scenarios where it will disappoint the frenzy in the market for the stock then there are positives for a company with such high valuations.
The phrase “the bigger they are, the harder they fall” was used in the context of the stock market as early as the 19th century. In 1873, the New York Stock Exchange crashed, and many investors lost their life savings. The crash was blamed on the overvaluation of stocks. Since then, there have been a number of times the market and individual stocks have come to terms with the idea that the price has gotten ahead of itself, causing speculators to flee, causing a sharp decline.
Nvidia was big and has gotten bigger. As it relates to its report on second-quarter earnings on Wednesday, analysts have been expressing positive expectations. In a “buy the rumor sell the news” way of thinking, this alone may cause the stock price to deflate after the report.
An article published by Barron’s on August 21st points out, using data from WisdomTree, that Nvidia has a price-to-projected sales ratio, of 25, this jumps to a whopping 40 when using 12-month trailing sales.
The company now holds the distinction of having the highest price-to-sales ratio in the S&P 500. It is only the 100th company to hold that title since the 1960s. Tech companies had been in this position 27 times, and utilities a mere once. What has happened to these mega-cap hot stocks in the years to follow?
Image: Research conducted for stocks for the long run 6th edition, Jeremy Siegel w/ Jeremy Schwartz (2022).
Over the next year, the average price-to-sales monsters saw their price rise 12% versus the average for the market of 11%. Then, over the next three years, the average stock dropped 4% annually, compared to the market’s 9% rise. The relative fall off continued over the next five years, as the average for these stocks had fallen 2%, versus the market’s 10% gain, according to a book by Wharton economic professor Jeremy Siegel.
Image: Research conducted for stocks for the long run 6th edition, Jeremy Siegel w/ Jeremy Schwartz (2022).
As illustrated in the table above, the underperformance is even greater for the tech companies that hold the biggest price-to-sales ratios. And the median performance for stocks at these price-to-sales ratios is even worse.
Take Away
Based on its guidance, there is little doubt that Nvidia is on track to post results that are beyond enviable. At least a dozen equity analysts have recently raised price targets on NVDA. But can the news be good enough in light of its current pricing and the history of tech stocks that have come before? NVDA would have been a great stock to have bought months ago and held; the current probabilities, based on history, now suggest the run-up is over, and the potential for a decline within a year has increased dramatically.
Bitcoin and Ethereum had a bad day. After gaining a lot of upward momentum from late June after Blackrock, Fidelity, and Invesco filed to create bitcoin-related exchange traded funds (ETFs), the volatile assets have shown cryptocurrency investors that the bumpy ride is not yet over. What’s causing it this time? Fortunately, it is not fraud or wrongdoing creating the turbulence. Instead, three factors external to the business of trading, mining, or exchanging digital assets are at work.
Background
On Thursday, August 17, and accelerating on August 18, the largest cryptocurrencies dropped precipitously. Bitcoin even broke down and fell below the psychologically important $26,000 US dollar price level before bouncing. While some are pointing to CME options expiration on the third Friday of each month, most are pointing to a Wall Street Journal article, and blaming Elon Musk, as the reason the asset class was nudged off a small cliff. There are other less highlighted, but important, catalysts that added to the flash-crash; these, along with the WSJ story, will be explained below.
Smells like Musk
What could SpaceX, the company owned and run by Elon Musk, possibly have to do with a crypto selloff? On Thursday, the crypto market had a downward spike around 5 PM ET. It was just after the Wall Street Journal revealed a change in the accounting valuation of SpaceX’s crypto assets. Reportedly, SpaceX marked down the value of its bitcoin assets by a substantial $373 million over the past two years. Additionally, the company has executed on crypto asset divestitures as well. When the reduction took place is uncertain, but cryptocurrency holdings have been reduced both in terms of the amount of coins and the value each coin is held for on the books.
Elon Musk’s reputation is that of a forward thinker, and one that embraces, if not leads, technology. He has significant influence over cryptocurrency valuations, often instigating pronounced market fluctuations brought about by Musk’s influential posts on his social media company, X. The reduction coincides with a similar crypto reduction on the books of publicly held, Musk-led, Tesla (TSLA). The electric car manufacturer had previously disclosed in its annual earnings report that it had liquidated 75% of its bitcoin reserves.
While it should not be surprising that two companies stepped away from speculation on something unrelated to their business or lowered support for the still young blockchain technology, it gave a reason for a reaction to this and other festering dynamics.
Wary of Gary
The Chairman of the Securities and Exchange Commission (SEC), Gary Gensler, is viewed as a “Whack-a Mole” to crypto stakeholders that prefer more autonomy than regulation. Every time the SEC gets knocked down as a potential regulator, it resurfaces, and crypto businesses have to deal with the agency again.
Last month, Judge Analisa Torres made a pivotal decision in a case involving payment company Ripple Labs and the Commission. Her verdict declared that a substantial portion of sales of the token XRP did not fall under the category of securities transactions. The SEC claimed it was a security. This judgement was hailed as a triumph for the crypto sector and catalyzed an impressive 20% uptick in the exchange Coinbase’s stock in a single day.
On the same Thursday as the WSJ article, the SEC showed its face again with a strong response to the earlier ruling. Judge Torres allowed the SEC’s request for an “interlocutory” appeal on her ruling. This process will involve the SEC presenting its motion, followed by Ripple’s counterarguments. This is slated to continue until mid-September. Afterward, the Judge will determine whether the agency can effectively challenge her token classification ruling in an appellate court.
The still young asset class, its exchange methods, valuation, and usage techniques, once they are more clearly defined, will serve to add stability and reduce risk and shocks in crypto and the surrounding businesses. The longer the legal system and regulatory entities take, including Congress, the longer it will take for cryptocurrencies to find the more settled mainstream place in the markets they desire.
Rate Spate
The eighteen-month-long spate of rate hikes in the U.S. and across the globe is providing an alternative investment choice instead of what are viewed as riskier assets. Coincidentally, again on Thursday, August 17, the ten-year US Treasury Note hit a yield higher than the markets have experienced in 12 years. At 4.31%, investors can lock in a known annual return for ten years that exceeds the current and projected inflation rate.
Take Away
The volatility in the crypto asset class has been dramatic – not for the weak-stomached investor. On the same day in August, three unrelated events together helped cause the asset class to spike down. These include an article in a top business news publication indicating that one of the world’s most recognized cryptocurrency advocates has reduced bitcoin’s exposure to his companies. The SEC being granted a rematch in a landmark case that it had recently lost, where the earlier outcome gave no provision for the SEC to treat cryptocurrencies like a security. And rounding out the triad of events on crypto’s throttleback Thursday, yields are up across the curve to levels not seen in a dozen years. Investor’s seeking a place to reduce risk can now provide themselves with interest payments in excess of inflation.
But despite the ups and downs, bitcoin is up 56.7% year-to-date, 11.1% over the past 12 months, 110.5% over three years, 300% over five years, and astronomical amounts over longer periods. Related companies like bitcoin miners, crypto exchanges, and blockchain companies have also experienced growth similar to that found in few other industries over the past decade.
Artificial neural networks, ubiquitous machine-learning models that can be trained to complete many tasks, are so called because their architecture is inspired by the way biological neurons process information in the human brain.
About six years ago, scientists discovered a new type of more powerful neural network model known as a transformer. These models can achieve unprecedented performance, such as by generating text from prompts with near-human-like accuracy. A transformer underlies AI systems such as ChatGPT and Bard, for example. While incredibly effective, transformers are also mysterious: Unlike with other brain-inspired neural network models, it hasn’t been clear how to build them using biological components.
Now, researchers from MIT, the MIT-IBM Watson AI Lab, and Harvard Medical School have produced a hypothesis that may explain how a transformer could be built using biological elements in the brain. They suggest that a biological network composed of neurons and other brain cells called astrocytes could perform the same core computation as a transformer.
Recent research has shown that astrocytes, non-neuronal cells that are abundant in the brain, communicate with neurons and play a role in some physiological processes, like regulating blood flow. But scientists still lack a clear understanding of what these cells do computationally.
With the new study, published this week in open-access format in the Proceedings of the National Academy of Sciences, the researchers explored the role astrocytes play in the brain from a computational perspective, and crafted a mathematical model that shows how they could be used, along with neurons, to build a biologically plausible transformer.
Their hypothesis provides insights that could spark future neuroscience research into how the human brain works. At the same time, it could help machine-learning researchers explain why transformers are so successful across a diverse set of complex tasks.
“The brain is far superior to even the best artificial neural networks that we have developed, but we don’t really know exactly how the brain works. There is scientific value in thinking about connections between biological hardware and large-scale artificial intelligence networks. This is neuroscience for AI and AI for neuroscience,” says Dmitry Krotov, a research staff member at the MIT-IBM Watson AI Lab and senior author of the research paper.
Joining Krotov on the paper are lead author Leo Kozachkov, a postdoc in the MIT Department of Brain and Cognitive Sciences; and Ksenia V. Kastanenka, an assistant professor of neurobiology at Harvard Medical School and an assistant investigator at the Massachusetts General Research Institute.
A Biological Impossibility Becomes Plausible
Transformers operate differently than other neural network models. For instance, a recurrent neural network trained for natural language processing would compare each word in a sentence to an internal state determined by the previous words. A transformer, on the other hand, compares all the words in the sentence at once to generate a prediction, a process called self-attention.
For self-attention to work, the transformer must keep all the words ready in some form of memory, Krotov explains, but this didn’t seem biologically possible due to the way neurons communicate.
However, a few years ago scientists studying a slightly different type of machine-learning model (known as a Dense Associated Memory) realized that this self-attention mechanism could occur in the brain, but only if there were communication between at least three neurons.
“The number three really popped out to me because it is known in neuroscience that these cells called astrocytes, which are not neurons, form three-way connections with neurons, what are called tripartite synapses,” Kozachkov says.
When two neurons communicate, a presynaptic neuron sends chemicals called neurotransmitters across the synapse that connects it to a postsynaptic neuron. Sometimes, an astrocyte is also connected — it wraps a long, thin tentacle around the synapse, creating a tripartite (three-part) synapse. One astrocyte may form millions of tripartite synapses.
The astrocyte collects some neurotransmitters that flow through the synaptic junction. At some point, the astrocyte can signal back to the neurons. Because astrocytes operate on a much longer time scale than neurons — they create signals by slowly elevating their calcium response and then decreasing it — these cells can hold and integrate information communicated to them from neurons. In this way, astrocytes can form a type of memory buffer, Krotov says.
“If you think about it from that perspective, then astrocytes are extremely natural for precisely the computation we need to perform the attention operation inside transformers,” he adds.
Building a Neuron-Astrocyte Network
With this insight, the researchers formed their hypothesis that astrocytes could play a role in how transformers compute. Then they set out to build a mathematical model of a neuron-astrocyte network that would operate like a transformer.
They took the core mathematics that comprise a transformer and developed simple biophysical models of what astrocytes and neurons do when they communicate in the brain, based on a deep dive into the literature and guidance from neuroscientist collaborators.
Then they combined the models in certain ways until they arrived at an equation of a neuron-astrocyte network that describes a transformer’s self-attention.
“Sometimes, we found that certain things we wanted to be true couldn’t be plausibly implemented. So, we had to think of workarounds. There are some things in the paper that are very careful approximations of the transformer architecture to be able to match it in a biologically plausible way,” Kozachkov says.
Through their analysis, the researchers showed that their biophysical neuron-astrocyte network theoretically matches a transformer. In addition, they conducted numerical simulations by feeding images and paragraphs of text to transformer models and comparing the responses to those of their simulated neuron-astrocyte network. Both responded to the prompts in similar ways, confirming their theoretical model.
“Having remained electrically silent for over a century of brain recordings, astrocytes are one of the most abundant, yet less explored, cells in the brain. The potential of unleashing the computational power of the other half of our brain is enormous,” says Konstantinos Michmizos, associate professor of computer science at Rutgers University, who was not involved with this work. “This study opens up a fascinating iterative loop, from understanding how intelligent behavior may truly emerge in the brain, to translating disruptive hypotheses into new tools that exhibit human-like intelligence.”
The next step for the researchers is to make the leap from theory to practice. They hope to compare the model’s predictions to those that have been observed in biological experiments, and use this knowledge to refine, or possibly disprove, their hypothesis.
In addition, one implication of their study is that astrocytes may be involved in long-term memory, since the network needs to store information to be able act on it in the future. Additional research could investigate this idea further, Krotov says.
“For a lot of reasons, astrocytes are extremely important for cognition and behavior, and they operate in fundamentally different ways from neurons. My biggest hope for this paper is that it catalyzes a bunch of research in computational neuroscience toward glial cells, and in particular, astrocytes,” adds Kozachkov.
Blackboxstocks, Inc. is a financial technology and social media hybrid platform offering real-time proprietary analytics and news for stock and options traders of all levels. Our web-based software employs “predictive technology” enhanced by artificial intelligence to find volatility and unusual market activity that may result in the rapid change in the price of a stock or option. Blackbox continuously scans the NASDAQ, New York Stock Exchange, CBOE, and all other options markets, analyzing over 10,000 stocks and up to 1,500,000 options contracts multiple times per second. We provide our users with a fully interactive social media platform that is integrated into our dashboard, enabling our users to exchange information and ideas quickly and efficiently through a common network. We recently introduced a live audio/video feature that allows our members to broadcast on their own channels to share trade strategies and market insight within the Blackbox community. Blackbox is a SaaS company with a growing base of users that spans 42 countries; current subscription fees are $99.97 per month or $959.00 annually. For more information, go to: www.blackboxstocks.com .
Joe Gomes, Managing Director, Equity Research Analyst, Generalist , Noble Capital Markets, Inc.
Joshua Zoepfel, Research Associate, Noble Capital Markets, Inc.
Refer to the full report for the price target, fundamental analysis, and rating.
2Q23. Revenue was $737,398, down from $1.4 million a year ago. Average monthly revenue per subscriber declined to $64.27 from $75.21 due to a higher level of promotional members during 2Q23. Blackboxstocks reported a net loss of $1.4 million for the quarter, or a loss of $0.45 per share, compared to a net loss of $1.3 million, or a loss of $0.40 per share in 2Q22. Adjusted EBITDA was a loss of $1.0 million, similar to the adjusted EBITDA loss in the year ago quarter.
Subscriber Counts. The average member count for the second quarter of 2023 was 3,937 compared to 5,482 for the second quarter of 2022 and 3,555 for the first quarter of 2023. The sequential stabilization of the member count reflects lower churn, while changes to marketing are expected to have a positive impact on the growth trajectory going forward.
Equity Research is available at no cost to Registered users of Channelchek. Not a Member? Click ‘Join’ to join the Channelchek Community. There is no cost to register, and we never collect credit card information.
This Company Sponsored Research is provided by Noble Capital Markets, Inc., a FINRA and S.E.C. registered broker-dealer (B/D).
*Analyst certification and important disclosures included in the full report. NOTE: investment decisions should not be based upon the content of this research summary. Proper due diligence is required before making any investment decision.
One Stop Systems, Inc. (OSS) designs and manufactures innovative AI Transportable edge computing modules and systems, including ruggedized servers, compute accelerators, expansion systems, flash storage arrays, and Ion Accelerator™ SAN, NAS, and data recording software for AI workflows. These products are used for AI data set capture, training, and large-scale inference in the defense, oil and gas, mining, autonomous vehicles, and rugged entertainment applications. OSS utilizes the power of PCI Express, the latest GPU accelerators and NVMe storage to build award-winning systems, including many industry firsts, for industrial OEMs and government customers. The company enables AI on the Fly® by bringing AI datacenter performance to ‘the edge,’ especially on mobile platforms, and by addressing the entire AI workflow, from high-speed data acquisition to deep learning, training, and inference. OSS products are available directly or through global distributors. For more information, go to www.onestopsystems.com.
Joe Gomes, Managing Director, Equity Research Analyst, Generalist , Noble Capital Markets, Inc.
Joshua Zoepfel, Research Associate, Noble Capital Markets, Inc.
Refer to the full report for the price target, fundamental analysis, and rating.
2Q23 Results. Revenue of $17.2 million, slightly below guidance and down 6% y-o-y, reflecting the continued runoff of the Disguise business. Higher operating expenses, including some one-time items, drove a net loss of $2.4 million, or a loss of $0.12/sh in the quarter, compared to net income of $322,822, or EPS of $0.02/sh per share last year. We had forecast a net loss of $415,700, or a loss of $0.02/sh. Adjusted EPS was $0.01 compared to $0.04 last year.
Delays and Departures. The decline in Disguise (substantially complete), delays in defense and commercial program orders, and the exit of an autonomous trucking client, a top 10 client in 2022, have combined to stall the momentum of OSS’ business. Revenue guidance for 3Q23 is $13.5 million, a level last seen in the first quarter of 2021.
Equity Research is available at no cost to Registered users of Channelchek. Not a Member? Click ‘Join’ to join the Channelchek Community. There is no cost to register, and we never collect credit card information.
This Company Sponsored Research is provided by Noble Capital Markets, Inc., a FINRA and S.E.C. registered broker-dealer (B/D).
*Analyst certification and important disclosures included in the full report. NOTE: investment decisions should not be based upon the content of this research summary. Proper due diligence is required before making any investment decision.
ISG (Information Services Group) (Nasdaq: III) is a leading global technology research and advisory firm. A trusted business partner to more than 700 clients, including more than 75 of the world’s top 100 enterprises, ISG is committed to helping corporations, public sector organizations, and service and technology providers achieve operational excellence and faster growth. The firm specializes in digital transformation services, including automation, cloud and data analytics; sourcing advisory; managed governance and risk services; network carrier services; strategy and operations design; change management; market intelligence and technology research and analysis. Founded in 2006, and based in Stamford, Conn., ISG employs more than 1,300 digital-ready professionals operating in more than 20 countries—a global team known for its innovative thinking, market influence, deep industry and technology expertise, and world-class research and analytical capabilities based on the industry’s most comprehensive marketplace data. For additional information, visit www.ISG-One.com
Joe Gomes, Managing Director, Equity Research Analyst, Generalist , Noble Capital Markets, Inc.
Joshua Zoepfel, Research Associate, Noble Capital Markets, Inc.
Refer to the full report for the price target, fundamental analysis, and rating.
Still Seeing Demand. ISG is continuing to experience increasing demand for its services, evidenced by the record second quarter revenue of $74.6 million. As noted in our previous report, recurring revenues now represents more than 40% of Company revenue and we anticipate the number to climb due to further demand from verticals such as consumer services, life sciences, and the public sector for the Company’s cost optimization services.
Balance Sheet. ISG reported a cash balance of $19.6 million as of June 30, 2023 compared to $23.7 million at March 31, 2023. The Company’s debt outstanding was at $79.2 million, unchanged from the first quarter, with the Company’s debt-to-EBITDA ratio remaining at 1.8x.
Equity Research is available at no cost to Registered users of Channelchek. Not a Member? Click ‘Join’ to join the Channelchek Community. There is no cost to register, and we never collect credit card information.
This Company Sponsored Research is provided by Noble Capital Markets, Inc., a FINRA and S.E.C. registered broker-dealer (B/D).
*Analyst certification and important disclosures included in the full report. NOTE: investment decisions should not be based upon the content of this research summary. Proper due diligence is required before making any investment decision.
ISG (Information Services Group) (Nasdaq: III) is a leading global technology research and advisory firm. A trusted business partner to more than 700 clients, including more than 75 of the world’s top 100 enterprises, ISG is committed to helping corporations, public sector organizations, and service and technology providers achieve operational excellence and faster growth. The firm specializes in digital transformation services, including automation, cloud and data analytics; sourcing advisory; managed governance and risk services; network carrier services; strategy and operations design; change management; market intelligence and technology research and analysis. Founded in 2006, and based in Stamford, Conn., ISG employs more than 1,300 digital-ready professionals operating in more than 20 countries—a global team known for its innovative thinking, market influence, deep industry and technology expertise, and world-class research and analytical capabilities based on the industry’s most comprehensive marketplace data. For additional information, visit www.ISG-One.com
Joe Gomes, Managing Director, Equity Research Analyst, Generalist , Noble Capital Markets, Inc.
Joshua Zoepfel, Research Associate, Noble Capital Markets, Inc.
Refer to the full report for the price target, fundamental analysis, and rating.
Record 2Q23 Top Line. Record 2Q revenueof $74.6 million, up 5.5% y-o-y. Currency translation negatively impacted reported revenue by $0.1 million. By geography, Americas revenue rose 7% to $42.3 million, Europe was up 5% on a reported basis to $24.4 million, and Asia-Pacific revenue of $8 million was flat to the prior year. We were at $75 million.
Increasing Recurring Revenues. Strong demand for research and platform services in the second quarter led to 21% growth in recurring revenues, which now represent more than 40% of Company revenue. ISG’s mix of portfolio solutions and services around cost optimization and digital transformation continues to resonate with clients.
Equity Research is available at no cost to Registered users of Channelchek. Not a Member? Click ‘Join’ to join the Channelchek Community. There is no cost to register, and we never collect credit card information.
This Company Sponsored Research is provided by Noble Capital Markets, Inc., a FINRA and S.E.C. registered broker-dealer (B/D).
*Analyst certification and important disclosures included in the full report. NOTE: investment decisions should not be based upon the content of this research summary. Proper due diligence is required before making any investment decision.
Plans for the Platform Formerly Known as “Twitter”
Elon Musk, who counts the old Twitter among the companies he oversees, has plans for a mega-financial component to the social media platform that has been rebranded as X. The serial entrepreneur has in the past discussed the “everything” app WeChat as a model for X’s direction. WeChat is a product available to banking clients in China, as a useful do-it-all tool chest. Musk says it has no equal in the U.S. Part of what is expected from Elon’s team is enabling users of X to trade stock and cryptocurrencies and also perform all that fintech companies like PayPal provide.
Embracing a New Direction
Molding X into the ultimate multi-functional app may be beginning to take shape and gain momentum. Musk may not have invented Twitter, but he plans on reinventing it with some very aggressive plans under the new name.
Evidence of this comes from Musk and his team’s discussions with a prominent financial data powerhouse to establish a trading hub within the X platform, including real-time market data. Leaked documents, as reported by the news source Semafor, and conversations with insiders have revealed the huge initiative. It is unclear whether X has secured partnerships for its additional direction at this point.
Fuzzy Business Benefit to Partnerships
X’s outreach to potential partners highlights the company’s promise of access to a massive social media user base numbering in the hundreds of millions. The proposal requests don’t mention compensation for the project, according to Liz Hoffman at Semafor.
Plans of incorporating a trading hub within the X platform have been brought up in the past. Not long ago eTORO, a unique social investment platform, had unveiled plans to facilitate the trading of various assets, including cryptocurrencies, directly to users, through a strategic partnership of what was then Twitter.
If the plans for an in-app trading hub materialize, given Musk’s evident familiarity with Dogecoin and other digital assets, X could potentially become a hub for cryptocurrency trading. Much of the the crypto regulatory world is still being written on a battlefield by various parties with different interests. This prospect might extend to established cryptocurrencies like bitcoin (BTC), which could be perceived as a relatively secure asset within some regulatory frameworks.
Elon Musk’s innovative drive is propelling X towards uncharted territory. As the app evolves, the prospect of a comprehensive trading hub integrated seamlessly within the platform could redefine the way users engage with their finances and investments. While details are understandably not public, knowledge that this may be unfolding and the potential power and disruption it may create are undeniable.
In a reply to a social post on X by @unusual_whales which read, “Twitter/X is planning to launch its own stock trading platform, per XNewsDaily,” Musk did not completely dismiss the existence of any plans but did not in any way confirm that there has been any real movement in this direction.
Critical Thinking and Analytical Skills Will Not Easily Be Replaced
Ever since the industrial revolution, people have feared that technology would take away their jobs. While some jobs and tasks have indeed been replaced by machines, others have emerged. The success of ChatGPT and other generative artificial intelligence (AI) now has many people wondering about the future of work – and whether their jobs are safe.
A recent poll found that more than half of people aged 18-24 are worried about AI and their careers. The fear that jobs might disappear or be replaced through automation is understandable. Recent research found that a quarter of tasks that humans currently do in the US and Europe could be automated in the coming years.
The increased use of AI in white-collar workplaces means the changes will be different to previous workplace transformations. That’s because, the thinking goes, middle-class jobs are now under threat.
The future of work is a popular topic of discussion, with countless books published each year on the topic. These books speak to the human need to understand how the future might be shaped.
This article was republished with permission from The Conversation, a news site dedicated to sharing ideas from academic experts. It represents the research-based findings and thoughts of, Elisabeth Kelan, Professor of Leadership and Organization, University of Essex.
I analyzed 10 books published between 2017 and 2020 that focused on the future of work and technology. From this research, I found that thinking about AI in the workplace generally falls into two camps. One is expressed as concern about the future of work and security of current roles – I call this sentiment “automation anxiety”. The other is the hope that humans and machines collaborate and thereby increase productivity – I call this “augmentation aspiration”.
Anxiety and Aspiration
I found a strong theme of concern in these books about technology enabling certain tasks to be automated, depriving many people of jobs. Specifically, the concern is that knowledge-based jobs – like those in accounting or law – that have long been regarded as the purview of well-educated professionals are now under threat of replacement by machines.
Automation undermines the idea that a good education will secure a good middle-class job. As economist Richard Baldwin points out in his 2019 book, The Globotics Upheaval, if you’ve invested a significant amount of money and time on a law degree – thinking it is a skill set that will keep you permanently employable – seeing AI complete tasks that a junior lawyer would normally be doing, at less cost, is going to be worrisome.
But there is another, more aspirational way to think about this. Some books stress the potential of humans collaborating with AI, to augment each other’s skills. This could mean working with robots in factories, but it could also mean using an AI chatbot when practicing law. Rather than being replaced, lawyers would then be augmented by technology.
In reality, automation and augmentation co-exist. For your future career, both will be relevant.
Future-Proofing Yourself
As you think about your own career, the first step is to realize that some automation of tasks is most likely going to be something you’ll have to contend with in the future.
In light of this, learning is one of the most important ways you can future-proof your career. But should you spend money on further education if the return on investment is uncertain?
It is true that specific skills risk becoming outdated as technology develops. However, more than learning specific abilities, education is about learning how to learn – that is, how to update your skills throughout your career. Research shows that having the ability to do so is highly valuable at work.
This learning can take place in educational settings, by going back to university or participating in an executive education course, but it can also happen on the job. In any discussion about your career, such as with your manager, you might want to raise which additional training you could do.
Critical thinking and analytical skills are going to be particularly central for how humans and machines can augment one another. When working with a machine, you need to be able to question the output that is produced. Humans are probably always going to be central to this – you might have a chatbot that automates parts of legal work, but a human will still be needed to make sense of it all.
Finally, remember that when people previously feared jobs would disappear and tasks would be replaced by machines, this was not necessarily the case. For instance, the introduction of automated teller machines (ATMs) did not eliminate bank tellers, but it did change their tasks.
Above all, choose a job that you enjoy and keep learning – so that if you do need to change course in the future, you know how to.
Full Disclosure of Limitations May Be the Quick Fix to AI Limitations
The Federal Trade Commission has launched an investigation of ChatGPT maker OpenAI for potential violations of consumer protection laws. The FTC sent the company a 20-page demand for information in the week of July 10, 2023. The move comes as European regulators have begun to take action, and Congress is working on legislation to regulate the artificial intelligence industry.
The FTC has asked OpenAI to provide details of all complaints the company has received from users regarding “false, misleading, disparaging, or harmful” statements put out by OpenAI, and whether OpenAI engaged in unfair or deceptive practices relating to risks of harm to consumers, including reputational harm. The agency has asked detailed questions about how OpenAI obtains its data, how it trains its models, the processes it uses for human feedback, risk assessment and mitigation, and its mechanisms for privacy protection.
This article was republished with permission from The Conversation, a news site dedicated to sharing ideas from academic experts. It represents the research-based findings and thoughts of, Anjana Susarla, Professor of Information Systems, Michigan State University.
As a researcher of social media and AI, I recognize the immensely transformative potential of generative AI models, but I believe that these systems pose risks. In particular, in the context of consumer protection, these models can produce errors, exhibit biases and violate personal data privacy.
Hidden Power
At the heart of chatbots such as ChatGPT and image generation tools such as DALL-E lies the power of generative AI models that can create realistic content from text, images, audio and video inputs. These tools can be accessed through a browser or a smartphone app.
Since these AI models have no predefined use, they can be fine-tuned for a wide range of applications in a variety of domains ranging from finance to biology. The models, trained on vast quantities of data, can be adapted for different tasks with little to no coding and sometimes as easily as by describing a task in simple language.
Given that AI models such as GPT-3 and GPT-4 were developed by private organizations using proprietary data sets, the public doesn’t know the nature of the data used to train them. The opacity of training data and the complexity of the model architecture – GPT-3 was trained on over 175 billion variables or “parameters” – make it difficult for anyone to audit these models. Consequently, it’s difficult to prove that the way they are built or trained causes harm.
Hallucinations
In language model AIs, a hallucination is a confident response that is inaccurate and seemingly not justified by a model’s training data. Even some generative AI models that were designed to be less prone to hallucinations have amplified them.
There is a danger that generative AI models can produce incorrect or misleading information that can end up being damaging to users. A study investigating ChatGPT’s ability to generate factually correct scientific writing in the medical field found that ChatGPT ended up either generating citations to nonexistent papers or reporting nonexistent results. My collaborators and I found similar patterns in our investigations.
Such hallucinations can cause real damage when the models are used without adequate supervision. For example, ChatGPT falsely claimed that a professor it named had been accused of sexual harassment. And a radio host has filed a defamation lawsuit against OpenAI regarding ChatGPT falsely claiming that there was a legal complaint against him for embezzlement.
Bias and Discrimination
Without adequate safeguards or protections, generative AI models trained on vast quantities of data collected from the internet can end up replicating existing societal biases. For example, organizations that use generative AI models to design recruiting campaigns could end up unintentionally discriminating against some groups of people.
When a journalist asked DALL-E 2 to generate images of “a technology journalist writing an article about a new AI system that can create remarkable and strange images,” it generated only pictures of men. An AI portrait app exhibited several sociocultural biases, for example by lightening the skin color of an actress.
Data Privacy
Another major concern, especially pertinent to the FTC investigation, is the risk of privacy breaches where the AI may end up revealing sensitive or confidential information. A hacker could gain access to sensitive information about people whose data was used to train an AI model.
Researchers have cautioned about risks from manipulations called prompt injection attacks, which can trick generative AI into giving out information that it shouldn’t. “Indirect prompt injection” attacks could trick AI models with steps such as sending someone a calendar invitation with instructions for their digital assistant to export the recipient’s data and send it to the hacker.
Some Solutions
The European Commission has published ethical guidelines for trustworthy AI that include an assessment checklist for six different aspects of AI systems: human agency and oversight; technical robustness and safety; privacy and data governance; transparency, diversity, nondiscrimination and fairness; societal and environmental well-being; and accountability.
Better documentation of AI developers’ processes can help in highlighting potential harms. For example, researchers of algorithmic fairness have proposed model cards, which are similar to nutritional labels for food. Data statements and datasheets, which characterize data sets used to train AI models, would serve a similar role.
Amazon Web Services, for instance, introduced AI service cards that describe the uses and limitations of some models it provides. The cards describe the models’ capabilities, training data and intended uses.
The FTC’s inquiry hints that this type of disclosure may be a direction that U.S. regulators take. Also, if the FTC finds OpenAI has violated consumer protection laws, it could fine the company or put it under a consent decree.
For Some White Collar Writing Tasks Chatbots Increased Productivity by 40%
Amid a huge amount of hype around generative AI, a new study from researchers at MIT sheds light on the technology’s impact on work, finding that it increased productivity for workers assigned tasks like writing cover letters, delicate emails, and cost-benefit analyses.
The tasks in the study weren’t quite replicas of real work: They didn’t require precise factual accuracy or context about things like a company’s goals or a customer’s preferences. Still, a number of the study’s participants said the assignments were similar to things they’d written in their real jobs — and the benefits were substantial. Access to the assistive chatbot ChatGPT decreased the time it took workers to complete the tasks by 40 percent, and output quality, as measured by independent evaluators, rose by 18 percent.
The researchers hope the study, which appears in open-access form in the journal Science, helps people understand the impact that AI tools like ChatGPT can have on the workforce.
“What we can say for sure is generative AI is going to have a big effect on white collar work,” says Shakked Noy, a PhD student in MIT’s Department of Economics, who co-authored the paper with fellow PhD student Whitney Zhang ’21. “I think what our study shows is that this kind of technology has important applications in white collar work. It’s a useful technology. But it’s still too early to tell if it will be good or bad, or how exactly it’s going to cause society to adjust.”
Simulating Work for Chatbots
For centuries, people have worried that new technological advancements would lead to mass automation and job loss. But new technologies also create new jobs, and when they increase worker productivity, they can have a net positive effect on the economy.
“Productivity is front of mind for economists when thinking of new technological developments,” Noy says. “The classical view in economics is that the most important thing that technological advancement does is raise productivity, in the sense of letting us produce economic output more efficiently.”
To study generative AI’s effect on worker productivity, the researchers gave 453 college-educated marketers, grant writers, consultants, data analysts, human resource professionals, and managers two writing tasks specific to their occupation. The 20- to 30-minute tasks included writing cover letters for grant applications, emails about organizational restructuring, and plans for analyses helping a company decide which customers to send push notifications to based on given customer data. Experienced professionals in the same occupations as each participant evaluated each submission as if they were encountering it in a work setting. Evaluators did not know which submissions were created with the help of ChatGPT.
Half of participants were given access to the chatbot ChatGPT-3.5, developed by the company OpenAI, for the second assignment. Those users finished tasks 11 minutes faster than the control group, while their average quality evaluations increased by 18 percent.
The data also showed that performance inequality between workers decreased, meaning workers who received a lower grade in the first task benefitted more from using ChatGPT for the second task.
The researchers say the tasks were broadly representative of assignments such professionals see in their real jobs, but they noted a number of limitations. Because they were using anonymous participants, the researchers couldn’t require contextual knowledge about a specific company or customer. They also had to give explicit instructions for each assignment, whereas real-world tasks may be more open-ended. Additionally, the researchers didn’t think it was feasible to hire fact-checkers to evaluate the accuracy of the outputs. Accuracy is a major problem for today’s generative AI technologies.
The researchers said those limitations could lessen ChatGPT’s productivity-boosting potential in the real world. Still, they believe the results show the technology’s promise — an idea supported by another of the study’s findings: Workers exposed to ChatGPT during the experiment were twice as likely to report using it in their real job two weeks after the experiment.
“The experiment demonstrates that it does bring significant speed benefits, even if those speed benefits are lesser in the real world because you need to spend time fact-checking and writing the prompts,” Noy says.
Taking the Macro View
The study offered a close-up look at the impact that tools like ChatGPT can have on certain writing tasks. But extrapolating that impact out to understand generative AI’s effect on the economy is more difficult. That’s what the researchers hope to work on next.
“There are so many other factors that are going to affect wages, employment, and shifts across sectors that would require pieces of evidence that aren’t in our paper,” Zhang says. “But the magnitude of time saved and quality increases are very large in our paper, so it does seem like this is pretty revolutionary, at least for certain types of work.”
Both researchers agree that, even if it’s accepted that ChatGPT will increase many workers’ productivity, much work remains to be done to figure out how society should respond to generative AI’s proliferation.
“The policy needed to adjust to these technologies can be very different depending on what future research finds,” Zhang says. “If we think this will boost wages for lower-paid workers, that’s a very different implication than if it’s going to increase wage inequality by boosting the wages of already high earners. I think there’s a lot of downstream economic and political effects that are important to pin down.”
The study was supported by an Emergent Ventures grant, the Mercatus Center, George Mason University, a George and Obie Shultz Fund grant, the MIT Department of Economics, and a National Science Foundation Graduate Research Fellowship Grant.
Comtech Telecommunications Corp. engages in the design, development, production, and marketing of products, systems, and services for advanced communications solutions in the United States and internationally. It operates in three segments: Telecommunications Transmission, Mobile Data Communications, and RF Microwave Amplifiers. The Telecommunications Transmission segment provides satellite earth station equipment and systems, over-the-horizon microwave systems, and forward error correction technology, which are used in various commercial and government applications, including backhaul of wireless and cellular traffic, broadcasting (including HDTV), IP-based communications traffic, long distance telephony, and secure defense applications. The Mobile Data Communications segment provides mobile satellite transceivers, and computers and satellite earth station network gateways and associated installation, training, and maintenance services; supplies and operates satellite packet data networks, including arranging and providing satellite capacity; and offers microsatellites and related components. The RF Microwave Amplifiers segment designs, develops, manufactures, and markets satellite earth station traveling wave tube amplifiers (TWTA) and broadband amplifiers. Its amplifiers are used in broadcast and broadband satellite communication; defense applications, such as telecommunications systems and electronic warfare systems; and commercial applications comprising oncology treatment systems, as well as to amplify signals carrying voice, video, or data for air-to-satellite-to-ground communications. The company serves satellite systems integrators, wireless and other communication service providers, broadcasters, defense contractors, military, governments, and oil companies. Comtech markets its products through independent representatives and value-added resellers. The company was founded in 1967 and is headquartered in Melville, New York.
Joe Gomes, Managing Director, Equity Research Analyst, Generalist , Noble Capital Markets, Inc.
Joshua Zoepfel, Research Associate, Noble Capital Markets, Inc.
Refer to the full report for the price target, fundamental analysis, and rating.
Troposcatter Contract. Yesterday, Comtech announced that the Company was awarded a contract from Fairwinds Technologies, LLC. The contract is for $30 million in which Comtech will provide the Company’s next-generation Troposcatter Family of Systems (FOS) in support of U.S. Army tactical communications. Under the contract, Comtech will be providing leading software-defined Troposcatter FOS to enhance U.S. Army Beyond-Line-of-Site communications across all domains. No timetable was given for the completion of the contract. The new contract complements the existing award from the U.S. Marines, in our view.
Fairwinds Technologies Overview. Fairwinds Technologies is a systems integrator and engineering services firm that designs and integrates communications, networking and information technology solutions to serve defense and civilian agencies around the world. Recent customers for the company include the U.S. Army and Defense Information Systems Agency.
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*Analyst certification and important disclosures included in the full report. NOTE: investment decisions should not be based upon the content of this research summary. Proper due diligence is required before making any investment decision.
The Legal Problems AI Now Creates Should Pave the Way to a Robust Industry
Is artificial intelligence, or more specifically OpenAI a risk to public safety? Can ChatGPT be ruining reputations with false statements? The Federal Trade Commission (FTC) sent a 20-page demand for records this week to OpenAI to answer questions and address risks related to its AI models. The agency is investigating whether the company engaged in unfair or deceptive practices that resulted in “reputational harm” to consumers. The results could set the stage defining the place artificial intelligence will occupy in the US.
Background
The FTC investigation into OpenAI began on March 2023. It resulted from a complaint from the Center for AI and Digital Policy (CAIDP). The complaint alleged that OpenAI’s ChatGPT-4 product violated Section 5 of the FTC Act. Section 5 prohibits unfair and deceptive trade practices. More specifically, CAIDP argues that ChatGPT-4 is biased, deceptive, and a risk to public safety.
The complaint cited a number of concerns about ChatGPT-4, including:
The model’s potential to generate harmful or offensive content.
The model’s tendency to make up facts that are not true.
The model’s lack of transparency and accountability.
The CAIDP also argued that OpenAI had not done enough to mitigate these risks. The complaint called on the FTC to investigate OpenAI and to take action to ensure that ChatGPT-4 is not used in a harmful way. The FTC has not yet made any public statements about the investigation. OpenAI has not commented publicly on the investigation.
It is not clear what action, if any, the FTC can or will take.
Negligence?
With few exceptions, companies are responsible for the harm done by their products when used correctly. One of the questions the FTC asked has to do with steps OpenAI has taken to address the potential for its products to “generate statements about real individuals that are false, misleading, or disparaging.” The outcome of this investigation, including any regulation could set the tone and define where responsibility lies regarding artificial intelligence.
As the race to develop more powerful AI services accelerates, regulatory scrutiny of the technology that could upend the way societies and businesses operate is growing. What is difficult is computer use generally isn’t isolated to a country, the internet extends far beyond borders. Global regulators are aiming to apply existing rules covering subjects from copyright and data privacy to the issues of data fed into models and the content they produce.
Legal Minefield
In a related story out this week, Comedian Sarah Silverman and two authors are suing Meta and OpenAI, alleging the companies’ AI language models were trained on copyrighted materials from their books without their knowledge or consent.
The copyright lawsuits against the ChatGPT parent and the Facebook parent were filed in a San Francisco federal court on Friday. Both suits are seeking class action status. Silverman, the author of “The Bedwetter,” is joined in her legal filing by authors Christopher Golden and Richard Kadrey.
Unlike the FTC complaint, the authors’ copyright suits may set a precedent on intelligence aggregation. The sudden birth of AI tools that have the ability to generate written work in response to user prompts was “taught” using real life work. The large language models at work behind the scenes of these tools are trained on immense quantities of online data. The training practice has raised accusations that these models may be pulling from copyrighted works without permission – most worrisome, these works could ultimately be served to train tools that upend the livelihoods of creatives.
Take Away
Investing in a promising new technology often means exposing oneself to a not yet settled legal framework. As the technology progresses, the early birds investing in relatively young and small companies may find they hold the next mega-cap company. Or, regulation may limit, to the point of stifling, the kind of growth experienced by Amazon and Apple a few short decades ago.
If AI follows the path of other technologies, well-defined boundaries, and regulations will give companies the confidence they need to invest capital in the technology’s future, and investors will be more confident in providing that capital.
The playing field is being created while the game is being played. Perhaps if the FTC has a list of 20 questions for OpenAI in ten years, it will just type them into ChatGPT and get a response in 20 seconds.
