Key Points: – Former Meta AR head Caitlin Kalinowski joins OpenAI to lead its robotics and hardware division. – OpenAI invests in Physical Intelligence, a $2.4 billion robotics startup, as part of its hardware push. – Kalinowski’s hire underscores OpenAI’s move to embed AI into consumer-facing, physical devices.
OpenAI has taken a major step in its robotics and hardware ambitions by hiring Caitlin “CK” Kalinowski, former head of Meta’s Orion augmented reality glasses project, to lead the company’s robotics and consumer hardware initiatives. Kalinowski, an experienced hardware engineer and executive, announced her new role on LinkedIn and X on Monday, stating that her initial focus at OpenAI will be “bringing AI into the physical world” through robotics work and strategic partnerships.
The move comes as OpenAI, best known for its chatbot ChatGPT, increasingly signals its intention to expand beyond software into physical technology. Kalinowski’s background includes nearly two and a half years at Meta leading the development of Orion, a pioneering AR glasses project initially known as Project Nazare, as well as nine years working on VR headsets for Meta’s Oculus division. Before her time at Meta, Kalinowski spent nearly six years at Apple, contributing to the design of MacBook Pro and MacBook Air models.
The timing of Kalinowski’s hiring aligns with OpenAI’s recent investment in Physical Intelligence, a robotics startup based in San Francisco that raised $400 million in funding. The investment round also saw contributions from high-profile investors including Amazon founder Jeff Bezos, Thrive Capital, Lux Capital, and Bond Capital, and the startup’s post-money valuation now stands at $2.4 billion. Physical Intelligence aims to bring general-purpose AI into real-world applications, using large-scale AI models and algorithms to power autonomous robots.
This latest move reflects OpenAI’s strategic push to establish itself as a leading force in consumer hardware, with a focus on embedding its AI capabilities into physical devices. This aligns with its recent partnership with Jony Ive, former Apple design chief, to conceptualize and develop an AI-driven consumer device. These developments indicate that OpenAI is not only aiming to develop software but is also working toward integrating its advanced AI capabilities into everyday, tangible products.
With the addition of Kalinowski, OpenAI gains expertise from a seasoned professional with a strong background in both augmented reality and consumer hardware, positioning the company to bring its AI advancements to life in ways that go beyond the digital realm. As OpenAI enters this new territory, Kalinowski’s experience in AR, VR, and consumer technology will likely be instrumental in helping the company transition its AI models from conceptual applications to real-world, user-friendly products.
Kalinowski’s start date at OpenAI is Tuesday, Nov. 5, marking the beginning of a new chapter for OpenAI as it takes significant strides toward expanding its footprint in robotics and consumer hardware.
Key Points: – Nvidia’s stellar earnings fail to impress investors as AI excitement wanes – Big Tech struggles to show concrete returns on massive AI investments – Nvidia’s diverse applications provide stability amid AI uncertainty
The artificial intelligence gold rush that has captivated Wall Street for the past 18 months is showing signs of cooling, as investors begin to demand more tangible results from the technology sector’s massive AI investments. This shift in sentiment was starkly illustrated by the market’s lukewarm response to Nvidia’s recent earnings report, which, despite showcasing impressive growth, failed to ignite the enthusiasm that has become characteristic of the AI narrative.
Nvidia, the world’s leading AI chip producer, delivered a quarterly report that would be the envy of most businesses. Sales surged 122% in the second quarter, profits doubled, and the outlook for the current quarter remained strong. Yet, Nvidia’s shares slumped 7% following the announcement, a telling indicator of changing investor expectations in the AI space.
The muted reaction to Nvidia’s stellar performance speaks volumes about the evolving psychology of Wall Street. For months, investors have been throwing money at any company with potential AI profits, creating a hype train that has carried Nvidia to a staggering 3,000% stock price increase over the past five years. The company’s quarterly earnings reports had taken on an almost mythical quality, consistently beating expectations and training Wall Street to anticipate the extraordinary.
However, the initial thrill of AI breakthroughs is beginning to fade, and investors are adopting a more discerning approach. The key question now is no longer about the potential of AI, but about its ability to generate concrete revenue for the companies heavily invested in its development. Big Tech firms have poured billions into AI research and development, yet have relatively little to show for it in terms of transformative products or services.
While chatbots like ChatGPT and Google Gemini have impressed, they haven’t quite lived up to the game-changing potential touted by their creators. The current consumer demand for AI seems centered on making mundane tasks less onerous, rather than the grand visions of AI revolutionizing creative processes or complex problem-solving that tech companies have been promoting.
For Nvidia, this reality check presents both challenges and opportunities. Unlike many AI startups built on promises and potential, Nvidia has a solid foundation in producing essential hardware for the tech industry. CEO Jensen Huang emphasized that Nvidia’s chips power not just AI chatbots, but also ad-targeting systems, search engines, robotics, and recommendation algorithms. The company’s data center business continues to drive nearly 90% of its total revenue, providing a stable base even as the AI hype cycle fluctuates.
However, Nvidia isn’t without its vulnerabilities. The company’s current dominance in AI chip production is partly due to the complexity and difficulty of replicating its products. But this advantage may not be permanent. Tech giants like Google and Amazon, currently reliant on Nvidia’s chips, are racing to develop their own AI hardware. The potential emergence of these customers as competitors could pose a significant threat to Nvidia’s market position in the long term.
As the AI landscape continues to evolve, investors are likely to become increasingly discriminating, focusing on companies that can demonstrate practical applications and revenue generation from their AI investments. For the tech industry as a whole, this shift may necessitate a recalibration of expectations and a more grounded approach to AI development and marketing.
The cooling of AI fever doesn’t signal the end of the technology’s potential. Rather, it marks a transition from unbridled enthusiasm to a more measured evaluation of AI’s place in the business world. As this reality check unfolds, companies that can bridge the gap between AI’s promise and its practical, revenue-generating applications will likely emerge as the true winners in this next phase of technological evolution.
The release of ChatGPT 4.0 by Anthropic has sent shockwaves through the tech world, with the AI model boasting unprecedented “human-level performance” across professional exams like the bar exam, SAT reading, and SAT math tests. As generative AI pioneers like OpenAI double down, one company has emerged as the indispensable force – Nvidia.
Nvidia’s cutting-edge GPUs provided the colossal computing power to train ChatGPT 4.0, which OpenAI hails as a seminal leap showcasing “more reliable, creative” intelligence than prior versions. The startup, backed by billions from Microsoft, turned to Microsoft Azure’s Nvidia-accelerated infrastructure to create what it calls the “largest” language model yet.
This scaling up of ever-larger foundational models at staggering financial costs is widely seen as key to recent AI breakthroughs. And Nvidia has established itself as the premier supplier of the high-performance parallelized hardware and software stack underpinning this generative AI revolution.
Major tech titans like Google, Microsoft, Meta, and Amazon are all tapping Nvidia’s specialized AI acceleration capabilities. At Google’s latest conference, CEO Sundar Pichai highlighted their “longstanding Nvidia partnership”, with Google Cloud adopting Nvidia’s forthcoming Blackwell GPUs in 2025. Microsoft is expected to unveil Nvidia-powered AI advancements at its Build event this week.
The AI chip wars are white-hot as legacy CPU makers desperately try dislodging Nvidia’s pole position. However, the chipmaker’s first-mover innovations like its ubiquitous CUDA platform have cemented its technological lead. Nvidia’s co-founder and CEO Jensen Huang encapsulated this preeminence, proudly declaring Nvidia brought “the most advanced” chips for OpenAI’s milestone AI demo.
With the AI accelerator market projected to swell into the hundreds of billions, Nvidia is squarely at the center of an infrastructure arms race. Hyperscalers are spending billions building out global AI-optimized data centers, with Meta alone deploying 350,000 Nvidia GPUs. Each breakthrough like GPT-4.0’s human-level exam performance reinforces Nvidia’s mission-critical role.
For investors, Nvidia’s lofty valuation and triple-digit stock gains are underpinned by blistering financial performance riding the generative AI wave. With transformative, open-domain AI models like GPT-4.0 being commercialized, Nvidia’s high-margin GPU cycles will remain in insatiable demand at the vanguard of the AI big bang.
Competitive headwinds will persist, but Nvidia has executed flawlessly to become the catalyzing force powering the most remarkable AI achievements today. As GPT-4.0 showcases tantalizing human-level abilities, Nvidia’s unbridled prowess in the AI chip arena shows no signs of waning.
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The artificial intelligence (AI) revolution has arrived, and big tech titans are betting their futures on it. Companies like Alphabet (Google), Microsoft, Amazon, Meta (Facebook), and Nvidia are pouring billions into developing advanced AI models, products, and services. For investors, this AI arms race presents both risks and immense opportunities.
AI is no longer just a buzzword – it is being infused into every corner of the tech world. Google has unveiled its AI chatbot Bard and AI search capabilities. Microsoft has integrated AI into its Office suite, email, browsing, and cloud services through an investment in OpenAI. Amazon’s Alexa and cloud AI services continue advancing. Meta is staking its virtual reality metaverse on generative AI after stumbles in social media. And Nvidia’s semiconductors have become the powerhouse engines driving most major AI systems.
The potential scope of AI to disrupt industries and create new products is staggering. Tech executives speak of AI as representing a tectonic shift on par with the internet itself. Beyond consumer services, AI applications could revolutionize fields like healthcare, scientific research, logistics, cybersecurity, and automation of routine tasks. The market for AI software, hardware, and services is projected to explode from around $92 billion in 2021 to over $1.5 trillion by 2030, according to GrandViewResearch estimates.
However, realizing this AI future isn’t cheap. Tech giants are locked in an AI spending spree, diverting resources from other business lines. Capital expenditures on computing power, AI researchers, and data are soaring into the tens of billions. Between 2022 and 2024, Alphabet’s AI-focused capex spending is projected to increase over 50% to around $48 billion per year. Meta recently warned investors it will “invest significantly more” into AI models and services over the coming years, even before generating revenue from them.
With such massive upfront investments required, the billion-dollar question is whether big tech’s AI gambles will actually pay off. Critics argue the current AI models remain limited and over-hyped, with core issues like data privacy, ethics, regulation, and potential disruptions still unresolved. The path to realizing the visionary applications touted by big tech may be longer and more arduous than anticipated.
For investors, therein lies both the risk and the opportunity with AI in the coming years. The downside is that profitless spending on AI R&D could weigh on earnings for years before any breakthroughs commercialize. This could pressure stock multiples for companies like Meta that lack other growth drivers. Major AI misses or public blunders could crush stock prices.
However, the upside is that companies driving transformative AI applications could see their growth prospects supercharged in lucrative new markets and business lines. Those becoming AI leaders in key fields and consumer services may seize first-mover advantages that enhance their competitive moats for decades. For long-term investors able to stomach volatility, getting in early on the next Amazon, Google, or Nvidia of the AI era could yield generational returns.
With hundreds of billions in capital flowing into big tech’s AI ambitions, investors would be wise to get educated on this disruptive trend shaping the future. While current AI models like ChatGPT capture imaginations, the real money will accrue to those companies pushing the boundaries of what AI can achieve into its next frontiers. Monitoring which tech companies demonstrate viable, revenue-generating AI use cases versus those with just empty hype will be critical for investment success. The AI revolution represents big risks – but also potentially huge rewards for those invested in its pioneers.
Nvidia’s share price has skyrocketed over 200% in 2023 alone, but some analysts believe the AI chip maker still has more gas in the tank for 2024. The meteoric rise has pushed Nvidia near trillion-dollar status, leading some to question how much higher the stock can climb. However, bullish analysts argue shares still look attractively priced given massive growth opportunities in AI computing.
Nvidia has emerged as the dominant player in AI chips, which are seeing surging demand from companies developing new generative AI applications. The company’s deals this year with ServiceNow and Snowflake for its H100 chip underscore how major tech firms are racing to leverage Nvidia’s graphics processing units (GPUs) to power natural language systems like ChatGPT.
This voracious appetite for Nvidia’s AI offerings has triggered a wave of earnings upgrades by analysts. Where three months ago Wall Street saw Nvidia earning $10.76 per share this fiscal year, the consensus forecast now stands at $12.29 according to Yahoo Finance data.
Next fiscal year, profits are expected to surge over 67% to $20.50 per share as Nvidia benefits from its pole position in the white-hot AI space. The upgraded outlooks have eased valuation concerns even as Nvidia’s stock price has steadily climbed to nosebleed levels.
Surge Driven by AI Dominance But Valuation Not Overstretched
Nvidia’s trailing P/E ratio now exceeds 65, but analysts note other metrics suggest the stock isn’t overly inflated. For example, Nvidia trades at a PEG ratio of just 0.5 times, indicating potential undervaluation for a hyper-growth company.
Its forward P/E of 24.5 also seems reasonable relative to expected 70%+ earnings growth next year. While far above the market average, analysts argue Nvidia deserves a premium multiple given its AI leadership and firm grasp on the emerging market.
Evercore ISI analyst Matthew Prisco sees a clear path for Nvidia to become the world’s most valuable company, surpassing Apple and Microsoft. But even if that lofty goal isn’t achieved, Prisco notes Nvidia still has ample room for expansion both in revenue and profits for 2024.
Other Catalysts to Drive Growth Despite Stellar Run
Prisco points to Nvidia expanding its customer base beyond AI startups to bigger enterprise players as one growth driver. Increasing production capacity for key AI chips like the H100 is another, which will allow Nvidia to capitalize on the AI boom.
Patrick Moorhead of Moor Insights & Strategy expects the untapped potential in inference AI applications to fuel Nvidia’s next leg higher. This is reminiscent of the machine learning surge that propelled Nvidia’s last massive rally around 2018.
While risks remain like potential profit-taking and Nvidia’s inability to sell advanced AI chips to China, analysts contend the long-term growth story remains solid. Nvidia is firing on all cylinders in perhaps the most disruptive tech space today in AI computing.
With its gaming roots and GPU headstart, Nvidia enjoys a competitive advantage over rivals in the AI chip race. And its platform approach working with developers and marquee customers helps feed an innovation flywheel difficult for challengers to replicate.
Final Thoughts on Nvidia’s Outlook
Nvidia has already achieved meteoric stock gains rarely seen for a mega-cap company. Yet analysts argue its leading position in the AI revolution merits an extended valuation premium despite the triple-digit surge.
Earnings estimates continue marching higher as customers clamor for Nvidia’s AI offerings. While the current P/E is lofty on an absolute basis, growth-adjusted valuations suggest upside remains as Nvidia cements it dominance across AI use cases.
If Nvidia can broaden its customer base, boost production capacity, and capitalize on emerging opportunities like inference AI, shares could continue to charge ahead despite their blistering 2023 rally. With tech titans racing to deploy the next generation of AI, Nvidia looks poised to provide the supercharged semiconductors powering this computing transformation.
Microsoft emerged victorious in the artificial intelligence talent wars by hiring ousted OpenAI CEO Sam Altman and other key staff from the pioneering startup. This coup ensures Microsoft retains exclusive access to OpenAI’s groundbreaking AI technology for its cloud and Office products.
OpenAI has been a strategic partner for Microsoft since 2019, when the software giant invested $1 billion in the nonprofit research lab. However, the surprise leadership shakeup at OpenAI late last week had sparked fears that Microsoft could lose its AI edge to hungry rivals.
Hiring Altman and other top OpenAI researchers nullifies this threat. Altman will lead a new Microsoft research group developing advanced AI. Joining him from OpenAI are co-founder Greg Brockman and key staff like Szymon Sidor.
The poaching also prevents Altman from jumping ship to competitors, according to analysts. “If Microsoft lost Altman, he could have gone to Amazon, Google, Apple, or a host of other tech companies,” said analyst Dan Ives of Wedbush Securities. “Instead he is safely in Microsoft’s HQ now.”
OpenAI Turmoil Prompted Microsoft’s Bold Move
The impetus for Microsoft’s talent grab was OpenAI’s messy leadership shakeup last week. Altman and other executives were reportedly forced out by OpenAI board chair.
The nonprofit recently created a for-profit subsidiary to commercialize its research. This entity was prepping for a share sale at an $86 billion valuation that would financially reward employees. But with Altman’s ouster, these lucrative payouts are now in jeopardy.
This uncertainty likely prompted top OpenAI staff to leap to the stability of Microsoft. Analysts believe more employees could follow as doubts grow about OpenAI’s direction under Emmett Shear.
Microsoft’s infrastructure and resources also make it an attractive home. The tech giant can provide the enormous computing power needed to develop ever-larger AI models. OpenAI’s latest system, GPT-3, required 285,000 CPU cores and 10,000 GPUs to train.
By housing OpenAI’s brightest minds, Microsoft aims to supercharge its AI capabilities across consumer and enterprise products.
The Rise of AI and Competition in the Cloud
Artificial intelligence is transforming the technology landscape. AI powers everything from search engines and digital assistants to facial recognition and self-driving cars.
Tech giants are racing to lead this AI revolution, as it promises to reshape industries and create trillion-dollar markets. This battle spans hardware, software and talent acquisition.
Microsoft trails category leader Google in consumer AI, but leads in enterprise applications. Meanwhile, Amazon dominates the cloud infrastructure underpinning AI development.
Cloud computing and AI are symbiotic technologies. The hyperscale data centers operated by Azure, AWS and Google Cloud provide the computational muscle for AI training. These clouds also allow companies to access AI tools on-demand.
This has sparked intense competition between the “Big 3” cloud providers. AWS currently has 33% market share versus 21% for Azure and 10% for Google Cloud. But Microsoft is quickly gaining ground.
Hiring Altman could significantly advance Microsoft’s position. His team can create exclusive AI capabilities that serve as a differentiator for Azure versus alternatives.
Microsoft’s Prospects in AI and the Stock Market
Microsoft’s big OpenAI poach turbocharged its already strong prospects in artificial intelligence. With Altman on board, Microsoft is better positioned than any rival to lead the next wave of AI innovation.
This coup should aid Microsoft’s fast-growing cloud business. New AI tools could help Microsoft chip away at AWS’s dominance while holding off Google Cloud.
If Microsoft extends its edge in enterprise AI, that would further boost revenue and earnings. This helps explain Wall Street’s positive reaction lifting Microsoft’s stock 1.5% and adding $30 billion in market value.
The success of cloud and AI has fueled Microsoft’s transformation from a stagnant also-ran to a Wall Street darling. Its stock has nearly tripled since early 2020 as earnings rapidly appreciate thanks to its cloud and subscription-based revenue.
Microsoft stock trades at a reasonable forward P/E of 25 and offers a dividend yield around 1%. If Microsoft keeps leveraging AI to expand its cloud business, its stock could have much further to run.
Hiring Altman and deploying OpenAI’s technology across Microsoft’s vast resources places a momentous technology advantage within the company’s grasp. Realizing this potential would be a major coup for Satya Nadella as CEO. With OpenAI’s crown jewels now safely in house, Microsoft’s tech lead looks more secure than ever.
There are Many Reasons for a Reverse Split; All are Designed to Benefit Stakeholders
So far this quarter, there have been 59 reverse stock splits. These include industries as diverse as the apparel company Digital Brands Group (DBGI), which is consolidating its shares today, and Blue Apron (APRN), an e-commerce food prep provider, back on July 8th. In theory, this is a financial arrangement similar to asking for a $100 bill in exchange for five $20 dollar bills. But the reasons are more complicated and diverse. Understanding why a company you own, or are considering buying or shorting shares in, is consolidating ownership units can help you understand if the new shares are more likely to gain or lose value.
Background
As with the exchange of smaller denominated bills for larger ones, a reverse stock split is an action in which a company reduces its total outstanding shares while proportionally increasing the price per new share. It’s done by the company’s registrar by combining a certain number of existing shares into a single new share. For example, a 1-for-10 reverse stock split would result in every 10 shares of the company being converted into 1 new share.
From the shareholder side, their percentage ownership in the company remains unchanged; the value of that percentage will change as market forces revalue those shares.
Reasons for a Reverse Split
A corporate action such as a reverse split is not inexpensive for the company, so if it is conducting one, it must see a benefit. The primary reasons range from crisis management to an attempt to broaden the share’s appeal.
The category of crisis management includes working to prevent delisting from an exchange. The major stock exchanges have minimum share price requirements. If a company’s stock price falls below this minimum, it will be delisted from the exchange. Back in March, Bed Bath and Beyond went to shareholders asking for permission to do a reverse split in order to not be delisted for having a stock price lower than the Nasdaq threshold. The company was criticized as it showed that management did not have confidence that the price would rise on its own. At times when a company is approaching the minimum threshold for being listed on an exchange, they will look to do a reverse split, this can boost the per share price and prevent delisting.
In some cases there isn’t a crisis; management is simply managing perception in an effort to improve the stock’s image. This is because a stock that trades at a low price may be perceived as being risky or unpopular. A reverse stock split can give the appearance of a more valuable stock, which may attract more investors.
Conforming to the requirements of certain buyers, specifically institutional investors may also lead to a reverse split. Many institutional investors have minimum investment requirements. A reverse stock split can help to make a stock more attractive to these investors.
Bringing up the dollar price to simplify trading is another reason. A reverse stock split can make it easier to trade a stock, especially if the shares have a price below one dollar.
TheCaution Signs When a Company Undergoes a Reverse Split
There are certainly potential negatives to shareholders when a company has a reverse stock split. For example, a reverse stock split can decrease liquidity, making it less liquid; for example, it may be more difficult to buy or sell.
Some investors may view a reverse stock split as a negative signal about the company’s financial health; if the action isn’t expected to cure the ailment, it may serve to feed into a growing list of things investors don’t like about the company.
Shareholders could wind up owning a lesser portion of the company if the split results in fractional shares. For example, if the stock you own 97 shares in reverse 1 for 10. You’ll receive 9 shares and, most often, the cash equivalent of seven shares.
Ultimately, whether or not a reverse stock split is a good idea for a company depends on the specific circumstances. Investors should carefully consider the pros and cons before making a decision about whether or not to buy or sell a stock that has undergone or is being talked about as considering a reverse stock split. In most cases only board of director approval is required.
Opportunity for Investors?
The opportunities for investors after a reverse stock split depends on the reasons for the split. If the split is done to prevent delisting, it is likely that the stock price will increase in the short term. However, if the split is done for other reasons, such as to improve the stock’s image or to make it more attractive to institutional investors, the long-term impact on the stock price is uncertain. Remember, management presumably got board approval as they thought it was in the best interest of the company; as a shareholder, you are technically an owner and would reap any benefit of it turning out to be a good move.
Take Away
A reverse stock split means the number of shares owned will be reduced, but the ownership level will remain the same. The price per share will increase, but the market capitalization of the company will change little. The reverse stock split may have a negative impact on the liquidity of the stock. It may also be seen as a negative by some investors.
Overall, reverse stock splits are always conducted for with the best interest of the company onwers in mind. But the reasons for the move, and if it will be successful needs to be evaluated by stockholders.
Industries are Impacted in Multiple Ways by Rising Gas Prices
High gas prices have again become a consideration when planning a roadtrip. The rollercoaster ride of very highs, then lows, and back again to highs, since the beginning of the decade has been numbing. The uncertainty of changing costs from one season to another has made it difficult for both businesses to plan, and for any individual driver, it all impacts decisions well beyond your weekend getaway.
Over the years, the price at the pump was driven up from factors such as geopolitical tensions, storms including hurricanes, a flood in Mississippi, and normal heightened demand during the summer driving season. On an individual level, increased gasoline costs translate to less disposable income for other necessities and luxuries. But, from an investor standpoint, the ramifications of soaring gas prices reach far beyond the service station, it deeply affect the broader economy and industry segments.
Conversely, plummeting gas prices mean lighter expenditures for households and businesses alike, relieving financial strains on transport-centric sectors such as airlines and trucking. However, these price drops also cast a shadow on some industries, especially the oil sector.
Below we highlight the direct and indirect adverse repercussions of high gas prices.
The Economic Ripples of Gas Prices
Employment
Job growth serves as a pivotal indicator of an economy’s recovery, and economists warn that surging gas prices could undermine hiring practices. Gas prices might prompt businesses to rethink their hiring plans, leading to a temporary hold as uncertainty about the economy’s well-being unfolds. Decreased discretionary spending and sales can influence a company’s hiring capacity.
Retailers
An indirect effect of soaring gas prices is a reduction in consumer discretionary spending due to a larger share of income being budgeted toward gasoline. Higher prices also induce consumers to drive less, even to places like malls and shopping centers. This is substantiated by both academic and industry studies, showing a direct correlation between driving miles and gas prices.
While shoppers might cut back on driving, they tend to increase online shopping when gas prices climb. Searches for online shopping surge in tandem with escalating gas prices.
Nonetheless, all retailers face added pressure to pass on the increased expenses they incur, particularly in shipping costs to consumers. Anything requiring transportation, from lumber to electronics, could incur higher costs due to surging gas prices. This holds true for products manufactured overseas or components sourced internationally. Moreover, products containing petroleum-derived materials or plastics also experience price hikes.
Auto Industry
Historically, the automobile industry responds to surging gas prices by producing smaller, more fuel-efficient vehicles, such as hybrids and all-electric cars capable of traveling long distances on a single charge. Consumers have shown robust support for this shift, evident from the upward trajectory of hybrid and all-electric vehicle sales in the United States since 2010, while sales of gas-guzzling trucks and SUVs lag.
Airlines
Fuel expenses are the single largest operational costs for airlines, constituting a substantial proportion of their overhead. Hence, fluctuations in oil prices significantly impact their bottom line. With rising gas prices, airlines are compelled to hike ticket prices, potentially discouraging non-essential air travel and burdening consumers financially.
To hedge against volatile oil costs, airlines often engage in fuel hedging, buying or selling expected future oil prices through various investment products. This strategy safeguards airlines from surging prices and sometimes even capitalizes on them.
New Jobs and Freelancers
Prospective job candidates must factor in commuting costs when evaluating potential positions. Some workers have had to decline job offers due to the exorbitant costs of commuting, consuming a significant chunk of their salary. Freelancers, most directly Uber and Lyft drivers, also feel the impact of higher gas prices, limiting their geographical scope of business as commuting expenses render some gigs unprofitable.
Take Away
Rising gas prices usually parallel a growing pessimism about the economy. While economists and analysts may debate the precise degree to which gas prices affect the economy, there undeniably exists a connection between consumer confidence, spending behaviors, and gas prices. This mood and actual level of sales have an impact on stock market activity, depending on how long fuel prices are expected to stay high (or low).
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.
Understanding the Distinction between Algorithm-Driven Functionality and Artificial Intelligence
Technological advancement doesn’t sleep. Rapidly evolving and unfolding, it is hard to keep up with the difference between, machine learning, artificial intelligence, and generative AI. Natural language processing and speech recognition also have massive overlaps, but are definitively different. Two “whiz-bang” technologies that are often confused, or at least the words have been used interchangeably are “artificial intelligence” and “algorithm-driven functionality.” While both concepts contribute to the advancement of technology, one would fall behind if they don’t understand the distinctions. Below we aim to clarify the dissimilarities between algorithm-driven functionality and artificial intelligence functionality, shedding light on their unique characteristics and applications will help investors understand the nature of companies they may be evaluating.
Algorithm-Driven Functionality
Algorithm-driven functionality primarily relies on predefined rules and step-by-step instructions to accomplish specific tasks. An algorithm is a sequence of logical instructions designed to solve a particular problem or achieve a specific outcome. Algorithms have been utilized for centuries, even before the advent of computers, to solve mathematical problems and perform calculations.
In state of the art technology, algorithms continue to play a crucial role. They are employed in search engines to rank web pages, in recommendation systems to suggest personalized content, in market analysis to indicate potential trades, and in sorting to organize data efficiently. Algorithm-driven functionality typically operates within predefined parameters, making it predictable and deterministic.
While algorithms are powerful tools, they lack the ability to learn or adapt to new situations. They require explicit instructions to perform tasks and cannot make decisions based on contextual understanding or real-time data analysis. Therefore, algorithm-driven systems may not best fit a complex situation with dynamic scenarios that demand flexibility and adaptability.
Artificial Intelligence Functionality
Artificial intelligence encompasses a broader set of technologies that enable machines to simulate human intelligence. AI systems possess the ability to perceive, reason, learn, and make decisions autonomously. Unlike algorithm-driven functionality, AI algorithms are capable of adapting and improving their performance through continuous learning from data.
Eventually they can have a mind of their own.
Machine learning (ML) is a prominent subset of AI that empowers algorithms to automatically learn patterns and insights from vast amounts of data. By analyzing historical information, ML algorithms can identify trends, make predictions, and generate valuable insights. Deep learning, a specialized branch of ML, employs artificial neural networks to process large datasets and extract intricate patterns, allowing AI systems to perform complex tasks such as image recognition and natural language processing.
AI functionality can be found in various applications across different sectors. Chatbots like ChatGPT can understand and respond to human queries, autonomous vehicles navigate and react to their surroundings, and recommendation systems that provide personalized suggestions are all examples of AI-driven technologies. These systems are capable of adapting to changing circumstances, improving their performance over time, and addressing complex, real-world challenges.
Differentiating Factors
The key distinction between algorithm-driven functionality and AI functionality lies in their capability to adapt and learn. While algorithms are rule-based and operate within predefined boundaries, AI algorithms possess the ability to learn from data, identify patterns, and modify their behavior accordingly. AI algorithms can recognize context, make informed decisions, and navigate uncharted territory with limited explicit instructions.
What freightens many is AI functionality exhibits a higher degree of autonomy compared to algorithm-driven systems. AI algorithms can analyze and interpret complex data, extract meaningful insights, and make decisions in real-time without relying on explicit instructions or human intervention. This autonomy enables AI systems to operate in dynamic environments where rules may not be explicitly defined, making them suitable for tasks that require adaptability and learning.
Take Away
Algorithm-driven functionality and artificial intelligence functionality are distinct concepts within the realm of technology. While algorithm-driven systems rely on predefined rules and instructions, AI functionality encompasses a broader set of technologies that enable machines to simulate human intelligence, adapt to new situations, and learn from data. Understanding these differences is crucial for leveraging the strengths of each approach for a given solution and harnessing the full potential of technology to solve complex problems and drive innovation to provide solutions and benefit.
Artificial intelligence (AI) is a true disruptive technology. As any informed content writer can tell you, the technology creates efficiencies by speeding up data gathering, research, and even graphics that specifically reflect the content. As an example, it is arguably quicker to use ChatGPT to provide a list of ticker symbols from company names, than it is to look them up one by one. With these small time savers, over the course of a week, far more can be produced as a result of AI tools saving a few minutes here and there.
This presents the question, what are the limits of AI – what can’t it do?
Worker Displacement
Technological revolutions have always benefitted humankind in the long run; in the short run, they have been disruptive, often displacing people who then have to retrain.
A new Goldman Sachs report says “significant disruption” could be on the horizon for the labor market. Goldman’s analysis of jobs in the U.S. and Europe shows that two-thirds of jobs could be automated at least to some degree. In the U.S., “of those occupations which are exposed, most have a significant — but partial — share of their workload (25-50%) that can be replaced,” Goldman Sachs’ analysts said in the paper.
Around the world, as many as 300 million jobs could be affected, the report says. Changes to labor markets are therefore likely – although historically, technological progress doesn’t just make jobs redundant, it also creates new ones. And the added productivity allows the masses to live wealthier lives. This clearly was the end result of the industrial revolution, and years after the computer revolution, we are at a high rate of employment and have at our fingertips much which we never even dreamed.
The Goldman report says the use of AI technology could boost labor productivity growth and boost global GDP by as much as 7% over time.
There are few reasons to expect that the AI revolution won’t also provide more goods and services per person for a richer existence. But, what about the disruption in the interim? I was curious to know what artificial intelligence is not expected to be able to do. There isn’t much information out there, so I went to an AI source and fed it a bunch of pointed questions about its nature. Part of that nature is to not intentionally lie, I found the responses worth sharing as we will all soon be impacted by what the technology can and cannot do.
Limitations of AI that Will Persist
Artificial intelligence has come a long way in recent years and the speed of progression and adoption is accelerating. As a result, applications have become increasingly sophisticated. But, there are still many things that AI cannot do now and may never be able to do.
One thing that AI cannot do now and may never be able to do is to truly understand human emotions and intentions. While AI algorithms can detect patterns in data and recognize certain emotional expressions, they do not have the ability to experience emotions themselves. This means that AI cannot truly understand the nuances of human communication, which can lead to misinterpretation and miscommunication.
Another limitation of AI is that it cannot replicate the creativity and intuition of humans. While AI can generate new ideas based on existing data, it lacks the ability to come up with truly original and innovative ideas. This is because creativity and intuition are often based on a combination of experience, emotion, and imagination, which are difficult to replicate in a machine.
AI also struggles with tasks that require common sense reasoning or context awareness. For example, AI may be able to identify a picture of a cat, but it may struggle to understand that a cat is an animal that can be petted or that it can climb trees. This is because AI lacks the contextual understanding that humans have built up through years of experience and interaction with the world around us.
In the realm of stocks and economics, AI has shown promise in analyzing data and making predictions, but there are still limitations to its abilities. For example, AI can analyze large datasets and identify patterns in market trends, but it cannot account for unexpected events or human behavior that may affect the market. This means that while AI can provide valuable insights, it cannot guarantee accurate predictions or prevent market volatility.
Another limitation of AI in economics is its inability to understand the complexities of social and political systems. Economic decisions are often influenced by social and political factors, such as government policies and public opinion. While AI can analyze economic data and identify correlations, it lacks the ability to understand the underlying social and political context that drives economic decisions.
A concern some have about artificial intelligence is that it may perpetuate biases that exist in the data it analyzes. This is the “garbage in, garbage out” data problem on steroids. For example, if historical data on stock prices is biased towards a certain demographic or industry, AI algorithms may replicate these biases in their predictions. This can lead to an amplified bias that proves faulty and not useful for economic decision making.
Take Away
AI has shown remarkable progress in recent years, but, as with everything that came before, there are still things that it cannot do now and may never be able to do. AI lacks the emotional intelligence, creativity, and intuition of humans, as well as common sense reasoning and social and political systems. In economics and stock market analysis, AI can provide valuable insights, but it cannot assure accurate predictions or prevent market volatility. So while companies are investing in ways to make our lives more productive with artificial intelligence and machine learning, it remains important to invest in our own human intelligence, growth and expertise.
Even a Short-Lived Default Would Hurt Money Market Fund Investors
While the U.S. Treasury is now at the mercy of politicians negotiating, positioning, and stonewalling as they work to raise the debt ceiling to avoid an economic catastrophe, money kept on the sidelines may be at risk. Generally, when investors reduce their involvement in stocks and other “risk-on” trades, they will park assets in money market funds. These investment products are now paying the highest interest rates in 15 years, which has made the decision to “take money off the table” even easier for those involved in the markets.
But, are investors experiencing a false sense of security?
Background
Money Market Funds (MMF) are mutual funds that invest in top credit-tier (low-risk) debt securities with fewer than 397 days to maturity. The SEC requires at least 10% to be maturing daily and 30% to be liquid within seven days. The acceptable securities in a general MMF include Treasury bills, commercial paper, and even bank CDs. The sole purpose of a money market fund is to provide investors with a stable value investment option with a low level of risk.
Unlike other mutual funds, money market funds are initially set and trade at a $1 price per share (NAV). As interest accrues, rather than the value of each share rising, investors are granted more shares (or fractional shares) at $1. However, the funds are marketed-to-market each day. Typically market prices don’t impact short-term debt securities at a rate above the daily interest accrual. But “typically” doesn’t mean always. Occasionally, asset values have dropped faster than the daily interest accrual. When this happens, the fund is worth less than $1 per share. It’s called “breaking the buck.”
When a money market fund “breaks the buck,” it means that the net asset value (NAV) per share of the fund falls below $1. In addition to quick valuation changes, it can also happen when the fund’s expense ratio exceeds its income. You may have gotten a notice during the extremely low interest period that your money market fund provider was absorbing expenses. This was to prevent it from breaking the buck.
Nothing is Risk Free
Just under $600 billion has moved into money-market funds in the past ten weeks. This is more than flowed into MM accounts after Lehman Brothers went belly up which set off panic and flights to safety. Currently, $5.3 trillion is invested in these funds; this is approaching an all-time record.
The Federal Reserve has been lifting interest rates at a record pace, the level they have the most control over is the bank overnight lending rate, or Fed Funds. This impacts short-term rates the most. Along with more attractive rates, stock market investors have become nervous. This is another reason asset levels in MMFs are so high – a high-yielding money-market fund that is viewed as risk-free looks attractive compared to the fear of getting caught in a stock market sell-off.
As discussed before, there are risks in money-market funds. And right now, the risks may be peaking. This is because government spending has exceeded the ability for the U.S. to borrow and pay for it under the current debt ceiling limit. The limit was actually reached last January when it was addressed by kicking the problem further down the road. Well, the road now ends sometime in June. In fact, U.S. Treasury Secretary Janet Yellen said the U.S. government may run out of cash by June 1 if Congress doesn’t act, and that economic chaos would ensue if the government couldn’t pay its obligations. Not paying obligations would include not paying interest on maturing U.S. Treasuries.
It isn’t a stretch to say the foundation of all other securities pricing is in relationship with the “risk-free” rate of U.S. debt. That is to say, price discovery has as its benchmark that which can be earned in U.S. debt which has been presumed to be without risk of non-payment.
What Happens to Money Market Funds in a Default?
In a default, the U.S. Treasury wouldn’t pay the full principle it owes on liabilities such as maturing Treasury debt – short term term government debt with extremely short average maturities is a staple of market funds. That is why the price of one-month Treasury debt has dropped recently, sending its yield up to above 5% from a 2023 low of about 3.3%. It has driven expected returns of MMFs up as well, but there is a risk that these short maturities may not get fully paid on time. Many fund providers’ money market funds would then break the $1 share price.
Breaking the buck can have significant consequences for investors, particularly those who rely on money market funds for their cash reserves. Because money market funds are considered a low-risk investment, investors may not expect to lose money on their investment. If a money market fund breaks the buck, it would diminish investor confidence in the stability of these funds, leading to a potential run on the fund and broader implications for the financial system.
Likelihood of Breaking the Buck
Money market funds breaking the buck is a relatively rare occurrence. According to the Securities and Exchange Commission (SEC), there have been only a few instances where MMFs have broken the buck in the history of the industry. The most significant of these occurred in 2008 during the financial crisis when one of the oldest money market funds, Reserve Primary Fund, dropped below $1 due to losses on its holdings of Lehman Brothers debt securities. This event led to a run on many money market funds creating significant instability in the financial system.
Since the Reserve Primary Fund incident, regulatory changes have been implemented to strengthen the money market fund industry and reduce the risk of funds breaking the buck. These changes include requirements for funds to maintain a minimum level of liquidity, hold more diversified portfolios, and limit their exposure to certain types of securities.
Take Away
Nothing is risk-free. Banks such as Silicon Valley Bank found that out when their investment portfolio, largely low credit risk, normally stable securities, wasn’t valued at what they needed it to be worth to fund large withdrawals.
Stock market investors that were drawn in invest in to rising bond yields also found that when yields keep rising, the values of their portfolios can drop just as quickly as if they were invested in stocks during a sell-off. While no one truly expects the current tug-of-war over debt levels in Washington to lead to a U.S. default, one can’t be sure at a time when there have been many firsts that we thought could never happen in America.
