AI a New Favorite Among Retail Investors

Image Credit: Focal Foto (Flickr)

Recent Investment Trends Include Small-Cap Artificial Intelligence Stocks

C3 AI, sometimes written C3.ai, is an artificial intelligence platform that provides services for companies to build large-scale AI applications. Its stock had the fifth highest traded shares among Fidelity’s retail investors on Monday (February 6). This included a record-breaking $31.4 million worth of shares traded among the broker’s individual self-directed traders. According to Reuters, “Retail investors are piling up on small-cap firms that employ artificial intelligence amid intensifying competition between tech titans.”  The article points to Google and Microsoft as examples of companies that expect AI to be the next meaningful driver of growth.

Investors, for their part, are looking to get ahead of any acquisition spree that deep-pocketed companies may embark on, which could include buying the advanced technology by acquiring small-cap tech firms.

Focus Heightened by ChatGPT

The spotlight ChatGPT finds itself in, three months after its launch, is indicicative of the interest in this technology amongst investors and users. With applications as numerous than one can think up, the technology could outdate many services provided by tech companies like Alphabet (GOOGL), or Microsoft (MSFT) – big tech has catching up to do. This seems to have created a race by cash rich companies to not be disrupted and left behind.

Investor’s recent focus on small companies in this space prefer those that are concentrated in AI technology. One main reason is that small-cap or microcap firms in this space are likely to have AI as a more concentrated part of their business. The bet being that whether the small company continues to grow independently, or is acquired by a larger firm looking to instantly be par with current technology, doesn’t much matter, it is a win for the investor if either occurs.

And it is a win, C3 AI stock rallied 46% last week, and climbed another 6.5% on Monday. It is now up 146% year to date.

Other Companies Involved

SoundHound AI, provides a voice AI platform services, and Thailand-based security firm Guardforce AI have more than doubled so far this year, while analytics firm BigBear.AI has increased ninefold.

US-listed shares of Baidu Inc climbed after the Chinese search engine indicated it would complete an internal test of a ChatGPT-style project called “Ernie Bot” next month.

Shares of Microsoft, which supports ChatGPT parent OpenAI, had been ratcheting up over the past month. The company is expected to make an announcement on their AI gained 1.5% in premarket trading ahead of the AI plans this week. ​

Google-owner Alphabet Inc said this week it would launch Bard, a chatbot service for developers, alongside its search engine.

Take Away

Change in technology that leads to improvements in daily lives has always been a focus of investors betting on which companies will outlast the others with “the next big thing.” These companies start out as small growth companies as Apple (AAPL) did in 1976. Then, a number of paths lay ahead. They either grow on their own like the Jobs/Wozniak computer maker did, get acquired for an early payday for investors and other stakeholders, or they can be outcompeted leaving investors with a non-performing asset.

Channelchek is a platform that specializes in bringing data and research on small-cap companies, including many varieties of new technology, to the investors that insist on being informed before they place a trade. Discover more on the industries of tomorrow by signing up for notifications in your inbox from Channelchek by registering here.

Paul Hoffman

Managing Editor, Channelchek

Sources

https://www.reuters.com/markets/us/retail-investors-flock-small-cap-ai-firms-big-tech-battles-share-2023-02-07/

https://www.barrons.com/articles/c3-ai-stock-rally-bull-wall-street-51675441248?mod=Searchresults

Causes of Paused or  Halted Trading in Company Stocks

Image credit: Alex Proimos (Flickr)

Discovering Why Trading is Halted on One of Your Stocks

Fair and orderly trading is an admirable goal of any system of exchange. As part of this ideal, exchanges, the SEC, and brokers can temporarily halt trading in stocks. The impact of news, or tripped circuit breakers designed to decelerate snowballing reactions (both human and programmed reactions), are the most common reasons to halt trading. There have also been events when a computer glitch, either feeding into an exchange or into the exchange’s systems, has triggered a pause or a halt. A total of 77 stocks were reportedly halted after the opening on the NYSE (January 24). They were all labeled “LULD,” this code is used to indicate it was a volatility trading pause. But officials at the NYSE say they’re still looking into it.

Reasons to Halt Trading

Companies listed on a U.S. stock exchange are responsible for notifying the listing exchange about any announcements or corporate developments that might affect trading in its stock. These often include:

  • Changes related to the financial health of the company
  • Changes in key management individuals
  • Major corporate transactions like restructurings or mergers
  • Significant positive or negative information about its products
  • Legal or regulatory developments that affect the company’s ability to conduct business
  • A circuit breaker has been reached due to volatility

Stock Halt Codes

Each day the exchanges list stocks as they are paused or halted and include a code to indicate the reason. The codes help market participants understand for how long it may be halted and for what general reason. It’s a good idea to be familiar with the codes shown below.

LUDP or LULD: Volatility trading pause (high volatility risk for investors).

T1: News pending (halted to give investors of all varieties ample time to evaluate).

H10: This is not enacted by the exchange but instead by the SEC (could be any number of regulatory reasons).

Image: Two of the many stocks halted on January 24, 2023 (NYSE Website)

The reason for the recent multiple stock pauses was available immediately on the NYSE website. Many of the stocks showed they were opening down substantially; the exchange says they are looking into this further.  

There are also times when a circuit breaker stops trading across the market exchange. This is not the reason for the multiple pauses experienced in January, but also worth mentioning. There are three levels of halt based on size of the markets (S&P 500) move.

Level 1: 15-minute halt due to a 7% decrease from the S&P 500’s previous close

Level 2: 15-minute halt due to a 13% decrease from the S&P 500’s previous close

Level 3: Day-long halt due to a 20% decrease from the S&P 500’s previous close

Take Away

When the market opens and it is not business as usual, a lot of frustration can be saved by knowing market rules and finding resources to get a fast answer. While other traders wait for their favorite news service to report on it, going directly to the NYSE website to, in this case, get a listing of affected stocks and why, can put you ahead of those that are waiting for CNBC or another news outlet. Nasdaq also will post paused or halted stocks and use the same codes as above to indicate why.

Paul Hoffman

Managing Editor, Channelchek

Sources

https://www.forexfactory.com/news/1201898-nyse-trading-open-sees-unusual-number-of-halted

https://www.zerohedge.com/markets/market-goes-haywire-dozens-nyse-trading-halts-open-after-technical-glitch

https://www.finra.org/investors/investing/investment-products/stocks/trading-halts-delays-suspensions

https://www.bloomberg.com/news/articles/2023-01-24/nyse-sees-unusual-number-of-trading-halts-at-open-of-trading-ldacqfyp?srnd=premium

https://www.nyse.com/trade-halt-current

Sky High Meme Stocks Score First in 2023

Image Background: George Larcher (Flickr)

Meme Stocks are Putting Up a Strong Offense – Is this a Positive Sign for the Broader Market?

During the first three weeks of 2023, meme stocks and crypto tokens, often viewed in the same category, have scored early. Have meme stock investors now come off the sidelines after the poor performance last year? In 2022 they completely failed to repeat their historic 2021 wins. So the current rally is a great sign.

Successful meme trading occurs when there is a mass movement by retail accounts. So far in 2023, like flipping a New Year’s switch, retail is again causing a commotion. And by looking at the trending hashtags and cashtags on Reddit and Twitter, fans are also making an increased volume of noise.

Source: Koyfin

Looking at the 2023 performance chart above, the S&P 500 ($SPY) opened the year more positively than the prior year ended. While one obviously can not extrapolate out the current 1.59% return for the year, annualizing it helps bring the short period being measured into perspective. The overall market is running at a 30.50% pace this year. Wow.

The performance of GameStop ($GME), which was one of the original and among the most recognized meme stocks, is outperforming the overall market by double. While it is well off its high reached earlier this week, the above 3% return is running well ahead of the overall stock market.

The cryptocurrency in the group, the often maligned Dogecoin (DOGE.X), which is legendary as it started as a parody token, has been tracking Bitcoins (BTC.X) rise closely. DOGE is up over 18% on the year, averaging an increase near 1% per day.

AMC Entertainment ($AMC), which is off its high of almost 50% a few days ago, now has returned over 32% to those holding the stock. To put this in perspective, it has an annualized return in 2023, so far, of 628%. This likely has gotten ahead of itself, time will tell, but it is the clear MVP among the meme stocks to date.

Source: Koyfin

Last year the overall market, despite being down near 20%,, trounced the meme stocks that have thus far put in a stellar showing in 2023.

Is Meme Rally a Reason for Optimism?

Retail dollars coming in off the sidelines and mounting enough of a drive to force values up so quickly indicates a mood change that may play out elsewhere in the financial markets. The average trade size of retail is so small that it indicates a large wave of willingness, if not outright optimism, that putting money in play will lead to gains. Similar forces are causing money to move into mutual funds and ETFs, which serves to put upward pressure on the overall market.

Wall Street’s so-called “fear gauge,” the Volatility Index ($VIX) dropped on average 1% a day since the start of the year. This is a spectacular trend. It now stands near its long-term average of 21; a reading above 30 is considered bearish. The $VIX was last near these levels in April of last year. The overall market stood 15% higher back then compared to today.  

The Volatility Index has applications across digital assets as well. On a scale of 1-100, where 100 is overly greedy, The Crypto Fear and Greed Index stands near neutral at 52. This is also the most optimistic reading since April. It may be considered even more positive since the digital asset market is still digesting the “unprecedented” bankruptcy of crypto exchange FTX.

Meme mania has never been about macro; more about crowd behavior, commitment, and momentum. But there are fundamentals that are viewed by stock investors of all varieties that likely have fed into the burst of interest.  First, economic data suggests that inflation is trending lower. This deceleration lessens the need for the Federal Reserve to put the brakes on the economy. The enthusiasm is just more pronounced among this style of retail traders that are loud and proud. They serve as cheerleaders to captivate the imagination of more traditional investors.

Take Away

The overall financial markets opened with a sigh of relief in 2023. Meme stocks and crypto opened the year with extreme optimism. The optimism isn’t without cause; a number of factors point to a much better environment than the dismal returns of last year.

Will this contagion, led by many small accounts, inspire further the larger individual and institutional investors to commit investments in the broader markets, there are many signs that suggest the year is starting that way, fear of missing out will build with each day that the markets move in a positive direction.

Paul Hoffman

Managing Editor, Channelchek

Sources

https://www.barrons.com/articles/gamestop-amc-dogecoin-shiba-inu-stock-price-meme-51674062277?mod=hp_LEAD_1

https://www.barrons.com/market-data/indexes/vix

One Stop Systems (OSS) – New Military Contract


Wednesday, January 18, 2023

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 – Generalist Analyst, 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.

New Contract. One Stop Systems announced a new $3 million order from a prime military contractor. OSS commenced shipments in the fourth quarter of 2022, with the remaining units to be delivered in the first half of 2023. We view the announcement as further validation of OSS’s capabilities for military applications.

Details. Using its 4UV compute accelerator systems, OSS is upgrading a radar simulation system operated by the DoD Missile Defense Agency. The systems are being deployed in edge mobile radar systems and datacenters where they will be used for lab and field artificial intelligence training.


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A Dirty Challenge for Autonomous Vehicle Designers

Image Credit: Christine Daniloff (MIT)

Computers that Power Self-Driving Cars Could be a Huge Driver of Global Carbon Emissions

Adam Zewe | MIT News Office

In the future, the energy needed to run the powerful computers on board a global fleet of autonomous vehicles could generate as many greenhouse gas emissions as all the data centers in the world today.

That is one key finding of a new study from MIT researchers that explored the potential energy consumption and related carbon emissions if autonomous vehicles are widely adopted.

The data centers that house the physical computing infrastructure used for running applications are widely known for their large carbon footprint: They currently account for about 0.3 percent of global greenhouse gas emissions, or about as much carbon as the country of Argentina produces annually, according to the International Energy Agency. Realizing that less attention has been paid to the potential footprint of autonomous vehicles, MIT researchers built a statistical model to study the problem. They determined that 1 billion autonomous vehicles, each driving for one hour per day with a computer consuming 840 watts, would consume enough energy to generate about the same amount of emissions as data centers currently do.

The researchers also found that in over 90 percent of modeled scenarios, to keep autonomous vehicle emissions from zooming past current data center emissions, each vehicle must use less than 1.2 kilowatts of power for computing, which would require more efficient hardware. In one scenario — where 95 percent of the global fleet of vehicles is autonomous in 2050, computational workloads double every three years, and the world continues to decarbonize at the current rate — they found that hardware efficiency would need to double faster than every 1.1 years to keep emissions under those levels.

“If we just keep the business-as-usual trends in decarbonization and the current rate of hardware efficiency improvements, it doesn’t seem like it is going to be enough to constrain the emissions from computing onboard autonomous vehicles. This has the potential to become an enormous problem. But if we get ahead of it, we could design more efficient autonomous vehicles that have a smaller carbon footprint from the start,” says first author Soumya Sudhakar, a graduate student in aeronautics and astronautics.

Sudhakar wrote the paper with her co-advisors Vivienne Sze, associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Research Laboratory of Electronics (RLE); and Sertac Karaman, associate professor of aeronautics and astronautics and director of the Laboratory for Information and Decision Systems (LIDS). The research appears today in the January-February issue of IEEE Micro.

Modeling Emissions

The researchers built a framework to explore the operational emissions from computers on board a global fleet of electric vehicles that are fully autonomous, meaning they don’t require a backup human driver.

The model is a function of the number of vehicles in the global fleet, the power of each computer on each vehicle, the hours driven by each vehicle, and the carbon intensity of the electricity powering each computer.

“On its own, that looks like a deceptively simple equation. But each of those variables contains a lot of uncertainty because we are considering an emerging application that is not here yet,” Sudhakar says.

For instance, some research suggests that the amount of time driven in autonomous vehicles might increase because people can multitask while driving, and the young and the elderly could drive more. But other research suggests that time spent driving might decrease because algorithms could find optimal routes that get people to their destinations faster.

In addition to considering these uncertainties, the researchers also needed to model advanced computing hardware and software that didn’t exist yet.

To accomplish that, they modeled the workload of a popular algorithm for autonomous vehicles, known as a multitask deep neural network, because it can perform many tasks at once. They explored how much energy this deep neural network would consume if it were processing many high-resolution inputs from many cameras with high frame rates simultaneously.

When they used the probabilistic model to explore different scenarios, Sudhakar was surprised by how quickly the algorithms’ workload added up.

For example, if an autonomous vehicle has 10 deep neural networks processing images from 10 cameras, and that vehicle drives for one hour a day, it will make 21.6 million inferences each day. One billion vehicles would make 21.6 quadrillion inferences. To put that into perspective, all of Facebook’s data centers worldwide make a few trillion inferences each day (1 quadrillion is 1,000 trillion).

“After seeing the results, this makes a lot of sense, but it is not something that is on a lot of people’s radar. These vehicles could actually be using a ton of computer power. They have a 360-degree view of the world, so while we have two eyes, they may have 20 eyes, looking all over the place and trying to understand all the things that are happening at the same time,” Karaman says.

Autonomous vehicles would be used for moving goods, as well as people, so there could be a massive amount of computing power distributed along global supply chains, he says. And their model only considers computing — it doesn’t take into account the energy consumed by vehicle sensors or the emissions generated during manufacturing.

Keeping Emissions in Check

To keep emissions from spiraling out of control, the researchers found that each autonomous vehicle needs to consume less than 1.2 kilowatts of energy for computing. For that to be possible, computing hardware must become more efficient at a significantly faster pace, doubling in efficiency about every 1.1 years.

One way to boost that efficiency could be to use more specialized hardware, which is designed to run specific driving algorithms. Because researchers know the navigation and perception tasks required for autonomous driving, it could be easier to design specialized hardware for those tasks, Sudhakar says. But vehicles tend to have 10- or 20-year lifespans, so one challenge in developing specialized hardware would be to “future-proof” it so it can run new algorithms.

In the future, researchers could also make the algorithms more efficient, so they would need less computing power. However, this is also challenging because trading off some accuracy for more efficiency could hamper vehicle safety.

Now that they have demonstrated this framework, the researchers want to continue exploring hardware efficiency and algorithm improvements. In addition, they say their model can be enhanced by characterizing embodied carbon from autonomous vehicles — the carbon emissions generated when a car is manufactured — and emissions from a vehicle’s sensors.

While there are still many scenarios to explore, the researchers hope that this work sheds light on a potential problem people may not have considered.

“We are hoping that people will think of emissions and carbon efficiency as important metrics to consider in their designs. The energy consumption of an autonomous vehicle is really critical, not just for extending the battery life, but also for sustainability,” says Sze.

Reprinted with permission of MIT News” (http://news.mit.edu/)

Cathie Wood Shines Spotlight on Missed Opportunities of 2022

Image Credit: City of St Pete (Flickr)

Cathie Wood Reveals 2022’s Most Disruptive and Innovative Technologies

ARK Invest’s Cathie Wood penned a lookback-themed article about the innovations and disruptive companies of 2022. The purpose seemed to be to remind followers that although during the year, investors may have become disheartened with innovation, ‘look at the amazing opportunities that occurred.’ The innovations and companies highlighted were somewhat overlooked; following the path we are accustomed to from many breakthroughs, they fly under the radar. Then, suddenly they’re widely adopted. Below are many of her picks for innovation and companies she may now wish her funds held large positions in.

The Future of Internet

Suddenly everyone is talking about ChatGPT. According to Wood, artificial intelligence (AI), specifically, ChatGPT is advancing at a pace that is surprising even by standards set by earlier versions. This version of GPT-3, optimized for conversation, signed up one million users in just five days. By comparison, this onboarding of users is incredibly fast benchmarked against the original GPT-3, which took 24 months to reach the same level.

In 2022, TV advertising in the US underwent significant changes. Traditional, non-addressable, non-interactive TV ad spending dropped by 2% to $70 billion, according to Wood. Connected TV (CTV) ad spending on the same terms increased by 14% to ~$21 billion. Pure-play CTV operator Roku’s advertising platform revenue increased 15% year-over-year in the third quarter, the latest report available, while traditional TV scatter markets plummeted 38% year-over-year in the US. Roku maintained its position in the CTV market as the leading smart TV vendor in the US, accounting for 32% of the market.

Digital Wallets are replacing both credit cards and cash. In the category of offline commerce. They overtook cash as the top transaction method in 2020 and accounted for 50% of global online commerce volume in 2021. As an example of the growth, Square’s payment volume soared 193%, six times faster than the 30% increase in total retail spending 2019-2022 (relative to pre-COVID levels).

While overall e-commerce spending increased by 99% over the last three years, social commerce merchandise volume grew even faster. Shopify’s gross merchandise volume grew by 312%, almost four times faster than overall e-commerce and taking a significant share from other retail.

Underlying public blockchains continue to process transactions despite what may be going on surrounding the connected industries. Wood says it highlights that “their transparent, decentralized, and auditable ledgers could be a solution to the fraud and mismanagement associated with centralized, opaque institutions.” She explains, “After the FTX collapse, the share of trading volume on decentralized exchanges, which allow for trading without a central intermediary, rose 37% from 8.35% to 11.44%.

Genomic Revolution

Base editing and multiplexing have the potential to provide more effective CAR-T treatments for patients with otherwise incurable cancers. Cathie Wood provided an example from 2022 about a young girl in the UK with leukemia that went from hopeless in May to Canver-free in November.

In 2022 Dutch scientists at the Hubrecht Institute, UMC Utrecht, and the Oncode Institute used another form of gene editing called prime editing to correct the mutation that causes cystic fibrosis in human stem cells. Another example of how it is being adopted comes from  Korean researchers at Yonsei University that used prime editing successfully to treat liver and eye diseases in adult mice.

CRISPR gene editing in Cathie’s words, “has delivered functional cures for beta-thalassemia and sickle cell disease.” She gives examples: CRISPR Therapeutics and Vertex Pharmaceuticals which together have treated more than 75 patients, resulting in some well-publicized “functional cures”. They are expecting FDA approval for Exa-Cel, the treatment for sickle cell and beta thalassemia, in early 2023.

In the category the Ark Invest founder referred to as other cell and gene therapies, she says in 2022, regulators approved several landmark cell and gene therapies. The examples she used to highlight this are Hemgenix for the treatment of Haemophilia B, Zyntelgo for beta thalassemia, Skysona for cerebral adrenoleukodystrophy, Yescarta and Breyanzi for Non-Hodgkin lymphoma, Tecartus for mantle cell lymphoma, and Carvykti and Abecma for multiple myeloma.

Liquid biopsies, blood tests via molecular diagnostic testing are enabling the early detection of colorectal cancer which, if discovered at or before stage 1, have a five-year survival rate greater than 90%. Late-stage or metastatic cancers account for more than 55% of deaths over a five-year period, but only 17% of new diagnoses.

Autonomous Technology & Robotics

During 2022 electric vehicle maker Tesla sales increased by 49% even as automobile sales declined by 8%. Tesla’s share of total auto sales in the US has increased to 3.8% from 1.4% in three years.

During 2022, GM expanded its autonomous driving taxi service to most of San Francisco in the first large-scale rollout in a major US city. Then it launched in both Phoenix and Austin late in the year. The automaker with a stodgy reputation, managed to compress the time to commercialization from nine years in San Francisco to just 90 days in Austin. Tesla, for its part, expanded access to its FSD (full self-driving) beta software to all owners in North America who had requested access.

By January 4, 2023, both Amazon and Walmart had begun deliveries using drones in select US cities. Autonomous logistics technology is no longer futuristic and is likely to continue being adopted and expanded.

Across the top 50 medical device companies, 90% rely on 3D printing for prototyping, testing, and even in some cases printing medical devices.

In 2022, SpaceX nearly doubled the number of rockets it launched to 61. It reused the same rocket in as few as 21 days, a dramatic improvement over the 356 days required for its first rocket reuse. Private Space Exploration is a reality. 61 rockets is an average of more than one per week.

Take Away

Hedge fund manager Cathie Wood took the new year as an opportunity to communicate examples of game-changing innovation that the equity market largely ignored in 2022. She finds these as confidence building that the premise of many of her managed funds is with merit. More importantly, in the face of market headwinds and media criticism, she wants these examples to help boost investor confidence “that ARK’s strategies are on the right side of change.” She tells readers, “innovation solves problems and has historically gained share during turbulent times.”

Paul Hofman

Managing Editor, Channelchek

Source

https://ark-invest.com/

Organs-On-A-Chip Minimize Late-Stage Drug Development Failures

Image: Lung-on-a-Chip,  National Center for Advancing Translational Sciences (Flickr)

Organ-On-A-Chip Models Allow Researchers to Conduct Studies Closer to Real-Life Conditions – and Possibly Grease the Drug Development Pipeline

Bringing a new drug to market costs billions of dollars and can take over a decade. These high monetary and time investments are both strong contributors to today’s skyrocketing health care costs and significant obstacles to delivering new therapies to patients. One big reason behind these barriers is the lab models researchers use to develop drugs in the first place.

Preclinical trials, or studies that test a drug’s efficacy and toxicity before it enters clinical trials in people, are mainly conducted on cell cultures and animals. Both are limited by their poor ability to mimic the conditions of the human body. Cell cultures in a petri dish are unable to replicate every aspect of tissue function, such as how cells interact in the body or the dynamics of living organs. And animals are not humans – even small genetic differences between species can be amplified to major physiological differences.

Fewer than 8% of successful animal studies for cancer therapies make it to human clinical trials. Because animal models often fail to predict drug effects in human clinical trials, these late-stage failures can significantly drive up both costs and patient health risks.

To address this translation problem, researchers have been developing a promising model that can more closely mimic the human body – organ-on-a-chip.

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 Chengpeng Chen, Assistant Professor of Chemistry and Biochemistry, University of Maryland, Baltimore County

As an analytical chemist, I have been working to develop organ and tissue models that avoid the simplicity of common cell cultures and the discrepancies of animal models. I believe that, with further development, organs-on-chips can help researchers study diseases and test drugs in conditions that are closer to real life.

What are Organs-On-Chips?

In the late 1990s, researchers figured out a way to layer elastic polymers to control and examine fluids at a microscopic level. This launched the field of microfluidics, which for the biomedical sciences involves the use of devices that can mimic the dynamic flow of fluids in the body, such as blood.

Advances in microfluidics have provided researchers a platform to culture cells that function more closely to how they would in the human body, specifically with organs-on-chips. The “chip” refers to the microfluidic device that encases the cells. They’re commonly made using the same technology as computer chips.

Not only do organs-on-chips mimic blood flow in the body, these platforms have microchambers that allow researchers to integrate multiple types of cells to mimic the diverse range of cell types normally present in an organ. The fluid flow connects these multiple cell types, allowing researchers to study how they interact with each other.

This technology can overcome the limitations of both static cell cultures and animal studies in several ways. First, the presence of fluid flowing in the model allows it to mimic both what a cell experiences in the body, such as how it receives nutrients and removes wastes, and how a drug will move in the blood and interact with multiple types of cells. The ability to control fluid flow also enables researchers to fine-tune the optimal dosing for a particular drug.

The lung-on-a-chip model, for instance, is able to integrate both the mechanical and physical qualities of a living human lung. It’s able to mimic the dilation and contraction, or inhalation and exhalation, of the lung and simulate the interface between the lung and air. The ability to replicate these qualities allows researchers to better study lung impairment across different factors.

Bringing Organs-On-Chips to Scale

While organ-on-a-chip pushes the boundaries of early-stage pharmaceutical research, the technology has not been widely integrated into drug development pipelines. I believe that a core obstacle for wide adoption of such chips is its high complexity and low practicality.

Current organ-on-a-chip models are difficult for the average scientist to use. Also, because most models are single-use and allow only one input, which limits what researchers can study at a given time, they are both expensive and time- and labor-intensive to implement. The high investments required to use these models might dampen enthusiasm to adopt them. After all, researchers often use the least complex models available for preclinical studies to reduce time and cost.

This chip mimics the blood-brain barrier. The blue dye marks where brain cells would go, and the red dye marks the route of blood flow. Vanderbilt University/Flickr

Lowering the technical bar to make and use organs-on-chips is critical to allowing the entire research community to take full advantage of their benefits. But this does not necessarily require simplifying the models. My lab, for example, has designed various “plug-and-play” tissue chips that are standardized and modular, allowing researchers to readily assemble premade parts to run their experiments.

The advent of 3D printing has also significantly facilitated the development of organ-on-a-chip, allowing researchers to directly manufacture entire tissue and organ models on chips. 3D printing is ideal for fast prototyping and design-sharing between users and also makes it easy for mass production of standardized materials.

I believe that organs-on-chips hold the potential to enable breakthroughs in drug discovery and allow researchers to better understand how organs function in health and disease. Increasing this technology’s accessibility could help take the model out of development in the lab and let it make its mark on the biomedical industry.

The Pros, Cons, and Many Definitions of ‘Gig’ Work

Image Credit: Stock Catalog

What’s a ‘Gig’ Job? How it’s Legally Defined Affects Workers’ Rights and Protections

The “gig” economy has captured the attention of technology futurists, journalists, academics and policymakers.

“Future of work” discussions tend toward two extremes: breathless excitement at the brave new world that provides greater flexibility, mobility and entrepreneurial energy, or dire accounts of its immiserating impacts on the workers who labor beneath the gig economy’s yoke.

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 David Weil, Visiting Senior Faculty Fellow, Ash Center for Democracy Harvard Kennedy School / Professor, Heller School for Social Policy and Management, Brandeis University.

These widely diverging views may be partly due to the many definitions of what constitutes “gig work” and the resulting difficulties in measuring its prevalence. As an academic who has studied workplace laws for decades and ran the federal agency that enforces workplace protections during the Obama administration, I know the way we define, measure and treat gig workers under the law has significant consequences for workers. That’s particularly true for those lacking leverage in the labor market.

While there are benefits for workers for this emerging model of employment, there are pitfalls as well. Confusion over the meaning and size of the gig workforce – at times the intentional work of companies with a vested economic interest – can obscure the problems gig status can have on workers’ earnings, workplace conditions and opportunities.

Defining Gig Work

Many trace the phrase “gig economy” to a 2009 essay in which editor and author Tina Brown proclaimed: “No one I know has a job anymore. They’ve got Gigs.”

Although Brown focused on professional and semiprofessional workers chasing short-term work, the term soon applied to a variety of jobs in low-paid occupations and industries. Several years later, the rapid ascent of Uber, Lyft and DoorDash led the term gig to be associated with platform and digital business models. More recently, the pandemic linked gig work to a broader set of jobs associated with high turnover, limited career prospects, volatile wages and exposure to COVID-19 uncertainties.

The imprecision of gig, therefore, connotes different things: Some uses focus on the temporary or “contingent” nature of the work, such as jobs that may be terminated at any time, usually at the discretion of the employer. Other definitions focus on the unpredictability of work in terms of earnings, scheduling, hours provided in a workweek or location. Still other depictions focus on the business structure through which work is engaged – a staffing agency, digital platform, contractor or other intermediary. Further complicating the definition of gig is whether the focus is on a worker’s primary source of income or on side work meant to supplement income.

Measuring Gig Work

These differing definitions of gig work have led to widely varying estimates of its prevalence.

A conservative estimate from the Bureau of Labor Statistics household-based survey of “alternative work arrangements” suggests that gig workers “in non-standard categories” account for about 10% of employment. Alternatively, other researchers estimate the prevalence as three times as common, or 32.5%, using a Federal Reserve survey that broadly defines gig work to include any work that is temporary and variable in nature as either a primary or secondary source of earnings. And when freelancing platform Upworks and consulting firm McKinsey & Co. use a broader concept of “independent work,” they report rates as high as 36% of employed respondents.

No consensus definition or measurement approach has emerged, despite many attempts, including a 2020 panel report by the National Academies of Sciences, Engineering, and Medicine. Various estimates do suggest several common themes, however: Gig work is sizable, present in both traditional and digital workplaces, and draws upon workers across the age, education, demographic and skill spectrum.

Why it Matters

As the above indicates, gig workers can range from high-paid professionals working on a project-to-project basis to low-wage workers whose earnings are highly variable, who work in nonprofessional or semiprofessional occupations and who accept – by choice or necessity – volatile hours and a short-term time commitment from the organization paying for that work.

Regardless of their professional status, many workers operating in gig arrangements are classified as independent contractors rather than employees. As independent contractors, workers lose rights to a minimum wage, overtime and a safe and healthy work environment as well as protections against discrimination and harassment. Independent contractors also lose access to unemployment insurance, workers’ compensation and paid sick leave now required in many states.

Federal and state laws differ in the factors they draw on to make that call. A key concept underlying that determination is how “economically dependent” the worker is on the employer or contracting party. Greater economic independence – for example, the ability to determine price of service, how and where tasks are done and opportunities for expanding or contracting that work based on the individual’s own skills, abilities and enterprise – suggest a role as an independent contractor.

In contrast, if the hiring party basically calls the shots – for example, controlling what the individual does, how they do their work and when they do it, what they are permitted to do and not do, and what performance is deemed acceptable – this suggests employee status. That’s because workplace laws are generally geared toward employees and seek to protect workers who have unequal bargaining leverage in the labor market, a concept based on long-standing Supreme Court precedent.

Making Work More Precarious

Over the past few decades, a growing number of low-wage workers find themselves in gig work situations – everything from platform drivers and delivery personnel to construction laborers, distribution workers, short-haul truck drivers and home health aides. Taken together, the grouping could easily exceed 20 million workers.

Many companies have incentives to classify these workers as independent contractors in order to reduce costs and risks, not because of a truly transformed nature of work where those so classified are real entrepreneurs or self-standing businesses.

Since gig work tends to be volatile and contingent, losing employment protections amplifies the precariousness of work. A business using misclassified workers can gain cost advantages over competitors who treat their workers as employees as required by the law. This competitive dynamic can spread misclassification to new companies, industries and occupations – a problem we addressed directly, for example, in construction cases when I led the Wage and Hour Division and more recently in several health care cases.

The future of work is not governed by immutable technological forces but involves volitional private and public choices. Navigating to that future requires weighing the benefits gig work can provide some workers with greater economic independence against the continuing need to protect and bestow rights for the many workers who will continue to play on a very uneven playing field in the labor market.

Innovation Works Best as a Freewheeling Process Not Grand Design

Image credit: Marcus Herzberg (Pexels)

For Now, Innovation and Entrepreneurship Still Hold a High Place in the USA

Commentators worry that the United States might lose its dominance in innovation to Asian countries like China and Singapore. Many policymakers are intimidated by the R&D budgets of Asian countries and by their superior performance on international academic assessments. However, these concerns are misguided because the United States still dominates innovation.

The United States ranks second on the Global Innovation Index and scores the highest in the world on fifteen of eighty-one innovation indicators. The US innovation ecosystem continues to lead in the commercialization of research, and its universities are on the cutting edge of academic research. Other countries are expanding research budgets, but the United States’ genius is its ability to commercialize relevant innovations.

Innovations are only useful when they disrupt industries by transforming society and altering consumer preferences. Because innovations respond to market changes, anything can become an innovation, and the process is highly spontaneous. Unfortunately, too many countries are laboring under the assumption that government plans inevitably lead to innovation. Finding the next game changer is tremendously difficult due to the dynamism of consumer preferences.

US entrepreneurs appreciate that innovation is a freewheeling process rather than an object of grand design. That is why Silicon Valley, with its reverence for risk and failure, has been known for innovation. In her 2014 book, The Upside of Down: Why Failing Well is the Key to Success, Megan McArdle argues that the United States’ tolerance toward failure is a crucial pillar of prosperity because it promotes self-actualization, risk, and the continuous quest for innovation.

The United States’ rivals have eloquent five-year plans and extravagant budgets, but US innovation is undergirded by private institutions with a strong appetite for risk and iconoclastic thinking. Private venture-capital associations and research institutions searching for future pioneers are the primary players in US innovation. Government innovation plans are inherently conservative because they hinge on the success of targeted industries.

But, in the private sector, entrepreneurs are deliberately scouting for disruptors to undercut traditional industries by launching breakthrough products. The conformity of government bureaucracies is an enemy of the unorthodox thinking that spurs innovation. China is known for having a competent and meritocratic civil service, yet scholars contend that it lacks an innovative environment.

A key problem is that China focuses on competing with western rivals instead of developing new industries; innovation is perceived as a competition between China and its rivals rather than an activity pursued for its own sake. Consequently, US companies remain market leaders and are more adept at converting market information into innovative products than their Chinese counterparts. Unlike China, US entrepreneurship is not a function of geopolitics.

Meanwhile, some commentators suggest that the US education system is better at deploying talent due to its encouragement of unorthodox thinking. In contrast, Singapore and China have been criticized for emphasizing rote learning at the expense of critical thinking. For example, Singapore’s public sector is a model of excellence; however, despite government support, Singapore is yet to become an innovation hotbed.

Bryan Cheung, in an assessment of industrial policy in Singapore, comments on the failure of Singapore to translate research into innovation: “Even though Singapore ranks highly on global innovation indices, closer scrutiny reveals that it scores poorly on the sub-component of innovation efficiency.” A recent edition of the Global Innovation Index, using a global comparison, declared that “Singapore produces less innovation outputs relative to its level of innovation investments.”

Cheung explains that Singapore is heavily reliant on foreign talent to boost innovation: “Even the six ‘unicorns’ that Singapore has produced (Grab, SEA, Trax, Lazada, Patsnap, Razer) were all founded or co-founded by foreign entrepreneurs. In the Start-Up Genome (2021), Singapore also performed relatively poorly in ‘quality and access’ to tech talent, research impact of publications, and local market reach, which is unsurprising since innovation activity is concentrated in foreign hands.”

Asian countries are growing more competitive, but it will take decades before they develop the United States’ appetite for risk, market-driven innovations, and the uncanny ability to monetize anything. The United States’ spectacular economic performance and business acumen are based on its unique culture. Those who bet against the United States by downplaying its culture are bound to lose. The United States’ rivals are still catching up.

About the Author

Lipton Matthews is a researcher, business analyst, and contributor to Merion WestThe FederalistAmerican Thinker, Intellectual Takeout, mises.org, and Imaginative Conservative. Visit his YouTube channel, here. He may be contacted at lo_matthews@yahoo.com or on Twitter (@matthewslipton)

Robinhood Stockholder’s Concern if SBF’s Holdings are Being Seized

Image Credit: Matt (Flickr)

Could There be an Impact on Robinhood Shareholders with the SBF Share Seizure

Creditors and customers of FTX may be able to reclaim some assets that were wiped out as the feds have been seizing the 7.50% stake in Robinhood (HOOD) stock held by Sam Bankman-Fried (SBF). SBF faces charges of fraud and a myriad of financial crimes after the collapse of FTX in November. The impact of the collapse is having an effect on other areas of finance, including assets that had been controlled by SBF. The Robinhood shares are valued near $450 million, and while this may bring some hope or relief to those that will receive a distribution, there is a risk to HOOD investors.

Background

The FTX bankruptcy has left a line of claimants to recapture what they can from the cryptocurrency giant. Bankruptcies are seldom easy; those that could involve layers of fraud become tied up in even larger disputes and legal battles. For example, the large Robinhood holding is tied up in a dispute between FTX and bankrupt crypto lender BlockFi. The company alleges that SBF put up the shares as collateral for a loan to Alameda Research, a company he also owned.

The HOOD stake was purchased in 2022 through a holding company SBF controlled, Robinhood of course is the innovative broker specializing in self-directed individual investors. Through the DOJ, authorities are going after the shares of HOOD and accounts that are held at the bank Silvergate Capital (SI) which is a banker for the crypto industry.

Separately, court filings on January 4th brought awareness to a NY federal judge ordered last month requiring the seizure of some $93 million that an FTX arm held in accounts at Silvergate. As it relates to this seizure. The Justice Department says it believes the assets seized are not the property of the bankruptcy estate, while a lawyer for FTX maintains that the seizures were from accounts not directly controlled by the company. They were ordered in connection with the criminal case involving SBF.  

 FTX investors’ asset claims in the exchange, which was once valued at $32 billion, come after creditors and other rightful claimants.

How This Could Impact Robinhood Shareholders

Asset seizures and later distribution to those hurt by fraud involve liquidation of the assets seized. In the case of stocks, they will be sold and turned into cash. Imagine a sudden effort to sell 7.50% of any company. That is a large percentage to move. The stake, worth between $400 and $500 million, may serve as a dark cloud depressing share prices and slowing any planned growth of the company. It may eventually culminate in liquidation at a pace not conducive to retaining a level stock price.

Paul Hoffman

Managing Editor, Channelchek

Sources

https://www.theblock.co/post/199271/doj-seizing-millions-in-robinhood-shares-linked-to-ftx-lawyer-says

https://www.wsj.com/articles/judge-ordered-seizure-of-money-from-ftx-digital-markets-accounts-at-silvergate-11672866368

https://www.barrons.com/articles/ftx-robinhood-doj-assets-51672932192?mod=hp_LATEST

Should We Tax Robots?

Image credit: Steve Jurvetson (Flickr)

Could a Modest Levy Combat Automation’s Impact on Income Imbalance?

Peter Dizikes | MIT News Office

What if the U.S. placed a tax on robots? The concept has been publicly discussed by policy analysts, scholars, and Bill Gates (who favors the notion). Because robots can replace jobs, the idea goes, a stiff tax on them would give firms incentive to help retain workers, while also compensating for a dropoff in payroll taxes when robots are used. Thus far, South Korea has reduced incentives for firms to deploy robots; European Union policymakers, on the other hand, considered a robot tax but did not enact it. 

Now a study by MIT economists scrutinizes the existing evidence and suggests the optimal policy in this situation would indeed include a tax on robots, but only a modest one. The same applies to taxes on foreign trade that would also reduce U.S. jobs, the research finds.  

“Our finding suggests that taxes on either robots or imported goods should be pretty small,” says Arnaud Costinot, an MIT economist, and co-author of a published paper detailing the findings. “Although robots have an effect on income inequality … they still lead to optimal taxes that are modest.”

Specifically, the study finds that a tax on robots should range from 1 percent to 3.7 percent of their value, while trade taxes would be from 0.03 percent to 0.11 percent, given current U.S. income taxes.

“We came into this not knowing what would happen,” says Iván Werning, an MIT economist and the other co-author of the study. “We had all the potential ingredients for this to be a big tax, so that by stopping technology or trade, you would have less inequality, but … for now, we find a tax in the one-digit range, and for trade, even smaller taxes.”

The paper, “Robots, Trade, and Luddism: A Sufficient Statistic Approach to Optimal Technology Regulation,” appears in the advance online form in The Review of Economic Studies. Costinot is a professor of economics and associate head of the MIT Department of Economics; Werning is the department’s Robert M. Solow Professor of Economics.

A Sufficient Statistic: Wages

A key to the study is that the scholars did not start with an a priori idea about whether or not taxes on robots and trade were merited. Rather, they applied a “sufficient statistic” approach, examining empirical evidence on the subject.

For instance, one study by MIT economist Daron Acemoglu and Boston University economist Pascual Restrepo found that in the U.S. from 1990 to 2007, adding one robot per 1,000 workers reduced the employment-to-population ratio by about 0.2 percent; each robot added in manufacturing replaced about 3.3 workers, while the increase in workplace robots lowered wages about 0.4 percent.

In conducting their policy analysis, Costinot and Werning drew upon that empirical study and others. They built a model to evaluate a few different scenarios, and included levers like income taxes as other means of addressing income inequality.

“We do have these other tools, though they’re not perfect, for dealing with inequality,” Werning says. “We think it’s incorrect to discuss this taxes on robots and trade as if they are our only tools for redistribution.”

Still more specifically, the scholars used wage distribution data across all five income quintiles in the U.S. — the top 20 percent, the next 20 percent, and so on — to evaluate the need for robot and trade taxes. Where empirical data indicates technology and trade have changed that wage distribution, the magnitude of that change helped produce the robot and trade tax estimates Costinot and Werning suggest. This has the benefit of simplicity; the overall wage numbers help the economists avoid making a model with too many assumptions about, say, the exact role automation might play in a workplace.

“I think where we are methodologically breaking ground, we’re able to make that connection between wages and taxes without making super-particular assumptions about technology and about the way production works,” Werning says. “It’s all encoded in that distributional effect. We’re asking a lot from that empirical work. But we’re not making assumptions we cannot test about the rest of the economy.”

Costinot adds: “If you are at peace with some high-level assumptions about the way markets operate, we can tell you that the only objects of interest driving the optimal policy on robots or Chinese goods should be these responses of wages across quantiles of the income distribution, which, luckily for us, people have tried to estimate.”

Beyond Robots, an Approach for Climate and More

Apart from its bottom-line tax numbers, the study contains some additional conclusions about technology and income trends. Perhaps counterintuitively, the research concludes that after many more robots are added to the economy, the impact that each additional robot has on wages may actually decline. At a future point, robot taxes could then be reduced even further.  

“You could have a situation where we deeply care about redistribution, we have more robots, we have more trade, but taxes are actually going down,” Costinot says. If the economy is relatively saturated with robots, he adds, “That marginal robot you are getting in the economy matters less and less for inequality.”

The study’s approach could also be applied to subjects besides automation and trade. There is increasing empirical work on, for instance, the impact of climate change on income inequality, as well as similar studies about how migration, education, and other things affect wages. Given the increasing empirical data in those fields, the kind of modeling Costinot and Werning perform in this paper could be applied to determine, say, the right level for carbon taxes, if the goal is to sustain a reasonable income distribution.

“There are a lot of other applications,” Werning says. “There is a similar logic to those issues, where this methodology would carry through.” That suggests several other future avenues of research related to the current paper.

In the meantime, for people who have envisioned a steep tax on robots, however, they are “qualitatively right, but quantitatively off,” Werning concludes.

Reprinted with permission of MIT News” (http://news.mit.edu/)

TAAL Distributed Information Technologies (TAALF) – Terminating Research Coverage


Friday, December 23, 2022

TAAL Distributed Information Technologies Inc. delivers value-added blockchain services, providing professional-grade, highly scalable blockchain infrastructure and transactional platforms to support businesses building solutions and applications upon the BitcoinSV platform, and developing, operating, and managing distributed computing systems for enterprise users.

Joe Gomes, Senior Research Analyst, 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.

Terminating Research Coverage. As expected, TAAL announced the completion of the previously announced plan to take the Company private. Calvin Ayre has acquired all of the remaining TAALF common shares and now owns 100%. The transaction was approved by the Ontario Superior Court of Justice on December 21st. TAALF common shares will be de-listed from the Canadian Securities Exchange no later than the close of business on December 23, 2022. As a result, we are terminating research coverage of TAAL Distributed Information Technologies. Effective upon termination of coverage, investors should no longer rely on any of our prior research, financial estimates, or ratings for the Company.


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Nuclear Fusion Technology Could Be A $40 Trillion Market

Nuclear Fusion’s Potential to Be a Highly Disruptive Breakthrough with Investment Opportunities

Scientists at the Energy Department’s Lawrence Livermore National Laboratory (LLNL) in California announced the first-ever demonstration of fusion “ignition.” This means that more energy was generated from fusion than was needed to operate the high-powered lasers that triggered the reaction. More than 2 megajoules (MJ) of laser light were directed onto a tiny gold-plated capsule, resulting in the production of a little over 3 MJ of energy, the equivalent of three sticks of dynamite.

This important milestone is the culmination of decades’ worth of research and lots of trial and error, and it makes good on the hope that humanity will one day enjoy 100% clean and plentiful energy.

This article was republished with permission from Frank Talk, a CEO Blog by Frank Holmes
of U.S. Global Investors (GROW).
Find more of Frank’s articles here – Originally published December 19, 2022.

Unlike conventional nuclear fission, which produces highly radioactive waste and carries the risk of nuclear proliferation, nuclear fusion has no emissions or risk of cataclysmic disaster. That should please activists who support renewable, non-carbon-emitting energy sources such as wind and solar and yet oppose nuclear power.

75th Anniversary of Another Great American Invention, The Transistor

I think it’s only fitting that this breakthrough occurred not just in the U.S., the most innovative country on earth, but also on the 75th anniversary of the invention of the transistor.

Like fusion energy, the transistor’s importance can’t be overstated. Invented in December 1947 in New Jersey’s storied Bell Labs—also the birthplace of the photovoltaic cell, fiber optic cable, communications satellite, UNIX operating system and C programming language—the transistor made the 20th century possible. Everything we use and enjoy today, from our iPhones to our Teslas, wouldn’t exist without the seminal American invention.  

In 2021, the electric vehicle maker unveiled its proprietary application-specific integrated circuit (ASIC) for artificial intelligence (AI) training. The ASIC chip, believe it or not, boasts an unbelievable 50 billion transistors.

Private Investment in Fusion Technology Has Been Increasing

Getting your electricity from a commercial fusion reactor is still years if not decades away, but that hasn’t stopped money from flowing into the sector. This year, private investment is estimated to top $1 billion, following the record $2.6 billion that went into fusion research in 2021, according to BloombergNEF.  

Private Sector Investment in Nuclear Fusion May Top $1 Billion in 2022

At the moment, there aren’t any publicly traded fusion companies. However, Bloomberg has a Global Nuclear Theme Peers index that tracks listed companies with exposure to the industry, estimated by Bloomberg to one day achieve a jaw-dropping $40 trillion valuation. Some of the more recognizable names include Rolls-Royce, Toshiba, Hitachi and General Electric.

For the five-year period, the index of 64 “nuclear” stocks has advanced approximately 100%, compared to the MSCI World Index, up 38% over the same period.

The number of private firms involved in R&D continues to grow, raising the possibility that some will tap public markets in the coming years.

Among the largest is Commonwealth Fusion Systems, or CFS, which spun out of MIT’s Plasma Science and Fusion Center in 2018. The company raised $1.8 billion in December 2021, on top of the $250 million it had raised previously. Its investors include Bill Gates and Google, along with oil companies, venture capital firms and sovereign wealth funds. CFS claims to have the fastest, lowest cost solution to commercial fusion energy and is in the process of building a prototype that is set to demonstrate net energy gain by 2025.

Another major player is TAE Technologies. Located in California, the company has raised a total of $1.2 billion as of December 2022, from investors such as the late Paul Allen, Goldman Sachs, Google and the family office of Charles Schwab. TAE says it is developing a fusion reactor, scheduled to be unveiled in the early 2030s, that will generate electricity from a proton-boron reaction at an incredible temperature of 1 billion degrees.

Other contenders in the field include Washington State-based Helion Energy, Canada’s General Fusion and the United Kingdom’s Tokamak Energy. In February 2022, Tokamak broke a longstanding record by generating 59 MJ of energy, the highest sustained energy pulse ever.

As an investor, I would keep an eye on this space!

Solar Accounted For 45% Of All New Energy Capacity Growth In The U.S.

In the meantime, energy investors with an eye on the future still have renewable energy stocks to consider.

2022 has been a challenging year for the industry, with much of it facing supply constraints. According to Wood Mackenzie, total new solar installations in the U.S. were 18.6 gigawatts (GW), a 23% decrease from 2021.

Even so, solar accounted for 45% of all new electricity-generation capacity added this year through the end of the third quarter. That’s greater than any other energy source. Wind was in second place, representing a quarter of all new energy power, followed by natural gas at 21% and coal at 10%, its best year since 2013.

WoodMac expresses optimism in the next two years. Solar projects that were delayed this year due to supply issues may finally come online in 2023, and by 2024, the real effects of President Biden’s Inflation Reduction Act (IRA) should be felt. The U.K.-based research firm forecasts 21% average annual growth from 2023 through 2027, so now may be an opportune time to start participating.

One of our favorite plays right now is Canadian Solar, up more than 11% for the year. On Thursday of this week, the Ontario-based company announced that it would begin mass-producing high efficiency solar modules in the first quarter of 2023. Canadian Solar shares were up more than 1% last week, despite experiencing two down days on this week’s news of continued rate hikes into 2023.

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The BI Global Nuclear Theme Peers is an index not for use as a financial benchmark that tracks 64 companies exposed to nuclear energy research and production. The MSCI World Index is a free-float weighted equity index which includes developed world markets and does not include emerging markets.

Holdings may change daily. Holdings are reported as of the most recent quarter-end. The following securities mentioned in the article were held by one or more accounts managed by U.S. Global Investors as of (09/30/22): Tesla Inc., Canadian Solar Inc.