Douglas Davenport

AI's Ascendancy: A 2024 Perspective

Artificial Intelligence (AI) has transitioned from a futuristic concept to a tangible reality firmly embedded in our daily lives. As of 2024, AI's influence spans across various industries and aspects of our routines, instigating both transformative advancements and disruptive shifts. From revolutionizing healthcare and finance to reshaping education and entertainment, AI's prowess lies in driving innovation, automating tasks, and fundamentally altering how we work and interact with the world. This in-depth article provides a panoramic view of AI's current landscape, examining its advancements, applications, ethical dilemmas, and potential future trajectories.

AI Advancements in 2024

The year 2024 has witnessed significant strides in AI capabilities, particularly in the realm of natural language processing with the rise of Large Language Models (LLMs). These models, trained on colossal text datasets, showcase exceptional proficiency in generating text, translating languages, writing code, and even engaging in creative endeavors. OpenAI's GPT-4 leads the charge with its extensive knowledge base and contextual understanding, finding applications in content creation, customer service chatbots, and scientific research support. Meta's LLaMA 2, an open-source LLM, is gaining traction due to its accessibility and potential for customization, fostering innovation and research across diverse fields. Google's PaLM 2, powering Google's Bard chatbot, boasts impressive language understanding and generation capabilities, delivering real-time, interactive AI experiences.

The progress in AI extends beyond language to encompass multiple forms of data, thanks to the advancements in Multimodal AI. Models like OpenAI's CLIP bridge the gap between text and images, enabling tasks such as image search and caption generation, paving the way for more intuitive interactions between humans and AI. OpenAI's DALL-E 2 further exemplifies AI's creative potential by generating images from textual descriptions, with implications for design, advertising, and entertainment.

Reinforcement learning, a learning paradigm where AI agents learn through trial and error, continues to make significant inroads, particularly in gaming and robotics. DeepMind's AlphaGo and AlphaZero maintain their dominance in complex games like Go and chess, demonstrating the power of reinforcement learning in strategic decision-making. In robotics, reinforcement learning empowers robots to acquire complex skills like grasping and manipulation, propelling advancements in industrial automation and potentially leading to the development of household robots.

The landscape of AI deployment is also evolving, with the growing popularity of Edge AI. This involves deploying AI models directly on edge devices like smartphones and IoT devices, enabling faster and more efficient AI applications while reducing reliance on cloud computing and enhancing privacy. Edge AI is poised to make AI more accessible and personalized, with applications ranging from real-time image recognition on smartphones to smart home devices that learn and adapt to user preferences.

AI Applications Across Industries

AI's transformative influence permeates various sectors, revolutionizing traditional practices and unlocking new possibilities. In healthcare, AI is proving to be a game-changer, aiding in early disease detection through AI-powered medical imaging tools, enabling personalized medicine through AI algorithms that tailor treatment plans, and improving surgical precision with AI-driven robotic surgery. The pharmaceutical industry benefits from AI's ability to accelerate drug discovery by analyzing vast datasets and predicting potential drug candidates. AI-powered chatbots and virtual assistants are also enhancing patient care by providing information and support.  

The financial sector is experiencing a significant AI-driven transformation as well. Automation of tasks like data entry and fraud detection streamlines operations and reduces costs. AI algorithms are leveraged to analyze customer data and offer personalized investment recommendations and financial planning, while high-speed algorithmic trading powered by AI systems is impacting market dynamics and necessitating careful regulation.

In education, AI is ushering in a new era of personalized learning and intelligent tutoring. Adaptive learning platforms tailor content and instruction to individual student needs, providing targeted feedback and support. Intelligent tutoring systems offer personalized guidance and feedback, empowering students to master complex concepts at their own pace. AI is also automating the grading of assignments, allowing educators to dedicate more time to meaningful interactions with students.

The entertainment industry is also embracing AI's creative potential. AI tools are composing original music, challenging the boundaries between human and machine creativity. In video game development, AI algorithms contribute to the creation of more realistic and engaging game environments and characters. AI-powered systems analyze user preferences to recommend personalized movies, music, and other forms of entertainment, enhancing the user experience.

AI's role in transportation is pivotal, with significant implications for the development of autonomous vehicles and transportation safety. Although still under development, self-driving cars hold the promise of revolutionizing transportation by improving safety and accessibility. AI-powered systems are optimizing traffic flow and reducing congestion, leading to shorter commutes and improved air quality. Furthermore, AI's predictive maintenance capabilities are enhancing vehicle safety and reducing costs by predicting and preventing breakdowns.

The manufacturing sector is also reaping the benefits of AI-driven automation and optimization. AI-powered robots are taking on repetitive and dangerous tasks, boosting productivity and worker safety. AI algorithms analyze sensor data to predict equipment failures, minimizing downtime and maintenance costs. Additionally, AI-powered vision systems are revolutionizing quality control by inspecting products for defects, ensuring high standards and reducing waste.

Ethical Concerns and Challenges

While AI's advancements offer a plethora of opportunities, they also raise ethical concerns and challenges that demand careful consideration and proactive solutions. One pressing concern is the potential for bias and unfairness in AI algorithms, which can inherit biases from the data they are trained on, leading to discriminatory outcomes. Addressing this issue requires ensuring diverse and representative training data and implementing ongoing monitoring of AI systems.

Another challenge lies in the potential for job displacement due to AI's automation capabilities. While AI is expected to create new job opportunities, it is also likely to replace some existing ones, necessitating workforce adaptation and reskilling initiatives to mitigate the impact on employment.

The collection and use of personal data by AI systems raise privacy concerns. Safeguarding data security and protecting individuals' privacy are critical challenges that necessitate robust data protection measures and transparency in AI applications.

Furthermore, understanding how AI systems reach their conclusions is paramount for building trust and ensuring accountability. Developing transparent and explainable AI models, particularly in complex deep learning systems, remains an ongoing challenge that requires concerted efforts.

The Future of AI

The future of AI is brimming with possibilities and potential trajectories. The development of Artificial General Intelligence (AGI), an AI system possessing human-level intelligence and capabilities, continues to be a long-term aspiration. AGI has the potential to revolutionize society, but it also raises profound questions about its impact on humanity and the future of work.

As AI becomes increasingly pervasive, the need for regulation and governance is growing. Striking a delicate balance between fostering innovation and ensuring ethical and responsible AI use is a key challenge for policymakers.

Collaboration between humans and AI is anticipated to become more commonplace, with AI augmenting human capabilities and aiding in decision-making. The development of effective human-AI collaboration models is an ongoing area of research with significant implications for the future of work and productivity.

Ensuring the ethical and responsible development and use of AI is an imperative. Fostering a culture of ethical AI development, promoting transparency, and addressing bias and fairness are essential for harnessing AI's potential for good and creating a future where AI benefits all of humanity.

In conclusion, AI stands as a rapidly evolving field with transformative potential. Its impact spans across industries and facets of daily life, offering tremendous opportunities while also raising ethical concerns and challenges. As AI continues its trajectory of advancement, collaboration between researchers, developers, policymakers, and society as a whole is paramount. By working together, we can navigate the complexities, harness AI's potential for good, and ensure a future where AI serves as a force for positive change, benefiting all of humankind.

Douglas Davenport

THE NEW BAROMETER FOR SMART INVESTING

(GOOG), (GOOGL), (NVDA), (TMC), (DE), (CTVA), (NEE), (FSLR), (UNP), (FDX)

"If you don't like the weather, wait five minutes, and it will change.” This old adage has long been a humorous nod to the weather's fickle nature. 

But what if you didn't have to wait? What if you could predict that change before it happened? 

Weather prediction has come a long way since the first computerized forecast in 1950. Today, we're looking at a global weather forecasting system market worth a cool $2.7 billion. That's as of 2023, mind you. 

By 2029? It’s projected to hit $4.2 billion. Growth like that doesn't just fall from the sky.

Traditional weather models have been the heavyweights for years. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Weather Service's Global Forecast System (GFS) are titans in the field. 

They crunch numbers on supercomputers that make your latest smartphone look like a pocket calculator. Take the Cray XC40 used by ECMWF. It's pumping out quadrillions of calculations per second. Impressive, right?

But here's the catch. These models aren't perfect. Far from it. Generating a single forecast can take hours. Hours. In the fast-paced world of weather, that's an eternity. 

By the time the forecast is ready, Mother Nature’s already changed her mind. It's like trying to catch a greased pig – frustrating and often futile.

A 2021 study in the Journal of Geophysical Research laid it out plain as day. ECMWF and GFS? They're reliable for short-term forecasts, up to about 10 days. 

Beyond that? Their crystal ball gets mighty cloudy. And when it comes to extreme weather, they're often caught with their pants down.

Case in point: Hurricane Beryl in 2024. This Category 5 monster decided to crash the party in early July, becoming the earliest Atlantic storm of its kind on record. 

Instead of heading to Mexico as predicted, it decided Texas looked more appealing, leaving a trail of destruction in its wake.

But it's not just hurricanes keeping forecasters up at night. The World Meteorological Organization confirmed 2023 as the hottest year on record. 

Global temperatures soared 1.15°C above pre-industrial averages. July 3, 2023? Possibly the hottest day ever recorded globally. Talk about turning up the heat.

The National Oceanic and Atmospheric Administration (NOAA) paints an even grimmer picture. In 2023, the U.S. was hit by 28 weather and climate disasters. Each one racked up at least $1 billion in damages. That ties the record set in 2020. Climate change isn't just knocking at the door – it's kicking it down.

Enter artificial intelligence (AI). 

DeepMind's GraphCast is making waves in the forecasting world. It's churning out global weather forecasts in less than a minute. You read that right. Seconds, not hours. 

In fact, a 2023 study in Science found GraphCast outperformed ECMWF's high-resolution prediction system in 90% of 1,380 evaluation metrics. That's not just beating the competition – it's lapping them.

But GraphCast isn't the only AI superstar in town. 

DeepMind's NeuralGCM combines traditional physics-based methods with machine learning. The result? Potentially game-changing long-range predictions. 

We're talking climate patterns decades into the future. Imagine the implications for long-term planning and investment strategies.

These AI models are weather savants. They devour vast amounts of historical data, using advanced neural networks to learn complex atmospheric patterns. 

The payoff? More accurate predictions, delivered faster than you can say "partly cloudy with a chance of meatballs."

Needless to say, these are opening up a whole new world of possibilities. 

We're talking personalized, hyperlocal forecasts. Better extreme weather warnings. More effective climate change modeling. Where there's innovation, there's opportunity.

Let's break it down with some numbers that really matter. A study in the Journal of Advances in Modeling Earth Systems (2022) found AI models could potentially extend reliable hurricane track predictions from 3 to 5 days in advance. 

Two extra days of preparation time. Crucial for both safety and economic reasons.

Speaking of economics, the U.S. National Hurricane Center estimates each mile of coastline evacuated costs about $1 million. 

More accurate forecasts could mean fewer unnecessary evacuations. Millions saved. And it's not just about saving money. It's about using resources more effectively. 

The Federal Emergency Management Agency (FEMA) reported that pre-positioning resources for Hurricane Irma in 2017 saved an estimated $100 million in recovery costs. Better forecasts mean better resource allocation. 

So, where's the smart money going? 

Keep your eyes on tech giants like Alphabet (GOOGL, GOOG), parent company of DeepMind. Market cap of $1.75 trillion. Q2 2024 revenue of $88.3 billion, up 15% year-over-year. 

Don't forget about NVIDIA (NVDA). Their GPUs are the workhorses powering these AI models. FY 2024 revenue? $60.9 billion. That's a 126% year-over-year growth. 

But it's not just about the tech giants.

Companies like The Tomorrow Companies Inc. (TMC) are specializing in AI-powered weather forecasting services. Market cap of $2.1 billion. 2023 revenue of $450 million, up 35% year-over-year. They're proving that sometimes, it pays to be a specialist.

And let's not overlook the industries that stand to benefit from better forecasts. 

Agriculture giants like Deere & Company (DE) and Corteva Inc. (CTVA) are integrating weather data into their precision agriculture solutions. 

In the energy sector, companies like NextEra Energy (NEE) and First Solar (FSLR) are using improved weather predictions to optimize renewable energy production.

Even transportation companies like Union Pacific Corporation (UNP) and FedEx Corporation (FDX) stand to gain. Weather-related disruptions cost the transportation industry billions annually. Better forecasts could lead to significant efficiency improvements.

The bottom line? The AI revolution in weather forecasting isn't just changing how we predict the weather. It's creating a perfect storm of investment opportunities across multiple fields. 

I advise you to keep a close watch on this sector because we all know that sometimes the biggest opportunities come from seeing which way the wind is blowing before everyone else does. 

And right now, that wind is blowing towards AI-powered weather forecasting. Don't get left out in the cold.

Douglas Davenport

The AI Revolution in Banking: A Deep Dive into the Institutions Leading the Charge

Artificial Intelligence (AI) is no longer a futuristic concept in the banking industry. It's a reality, rapidly transforming how banks operate, serve customers, and make strategic decisions. From fraud detection to personalized financial advice, AI is permeating every facet of banking operations.

In this article, we'll delve into the current AI landscape in the banking sector, highlighting the institutions that are at the forefront of this technological revolution and we'll explore their specific AI implementations, the benefits they're reaping, and the challenges they face.

The Leaders of the Pack

While AI is being embraced across the board, some banking institutions stand out due to their extensive and innovative use of this technology. Let's spotlight a few of these pioneers:

  1. JPMorgan Chase:
  • AI implementations: JPMorgan Chase leverages AI for a myriad of purposes, including fraud detection, risk management, trading, and customer service. Their use of AI for algorithmic trading is particularly noteworthy. They've developed sophisticated AI models that can analyze market trends, identify trading opportunities, and execute trades at lightning speed.
  • Benefits: The benefits are manifold. AI has enabled JPMorgan Chase to improve operational efficiency, reduce costs, enhance customer experience, and make better-informed investment decisions.
  • Challenges: One of the main challenges is ensuring the explainability of AI models, especially in high-stakes areas like credit risk assessment.
  1. Capital One:
  • AI implementations: Capital One's Eno, an AI-powered chatbot, is a testament to their commitment to AI. Eno assists customers with a wide range of tasks, from checking balances to disputing transactions. Capital One also uses AI extensively for fraud detection, credit risk assessment, and marketing.
  • Benefits: AI has enabled Capital One to provide more personalized customer service, improve fraud detection rates, and make more targeted marketing campaigns.
  • Challenges: A key challenge is maintaining customer trust in AI-powered systems, especially when it comes to handling sensitive financial information.
  1. Wells Fargo:
  • AI implementations: Wells Fargo's use of AI spans from customer service to risk management. They've implemented AI-powered virtual assistants to handle customer inquiries, AI-based models for fraud detection, and AI algorithms for credit risk assessment.
  • Benefits: Wells Fargo has reported increased operational efficiency, reduced fraud losses, and improved customer satisfaction as a result of their AI initiatives.
  • Challenges: A persistent challenge is ensuring the fairness and non-discrimination of AI algorithms, especially in areas like lending.
  1. Bank of America:
  • AI implementations: Bank of America's virtual assistant, Erica, is a prime example of their AI prowess. Erica can help customers with a variety of tasks, from checking balances to setting up bill payments. Bank of America also leverages AI for fraud detection, risk management, and investment advice.
  • Benefits: Erica has been instrumental in enhancing customer engagement and satisfaction. AI has also helped Bank of America improve its operational efficiency and risk management practices.
  • Challenges: A major challenge is keeping up with the rapid pace of AI advancements and ensuring their AI systems remain at the cutting edge.
  1. Citigroup:
  • AI implementations: Citigroup uses AI extensively across its operations, from customer service to investment banking. They've developed AI-powered chatbots to handle customer queries, AI-based models for fraud detection, and AI algorithms for trading and risk management.
  • Benefits: AI has enabled Citigroup to provide more efficient and personalized customer service, enhance its risk management practices, and make more informed investment decisions.
  • Challenges: A key challenge is ensuring the ethical use of AI, especially in areas like customer profiling and targeted marketing.

Specific AI Use Cases in Banking

AI is not just a buzzword in the banking sector. It's being applied to a wide range of use cases, each with its own set of benefits and challenges. Let's explore some of the most prominent ones:

  1. Customer Service:
  • AI-powered chatbots and virtual assistants are increasingly being used to handle customer inquiries, provide account information, and assist with basic transactions.
  • Benefits: This frees up human agents to focus on more complex issues, leading to improved customer satisfaction and reduced costs.
  • Challenges: Ensuring chatbots can handle a wide range of queries and provide accurate information can be a challenge.
  1. Fraud Detection:
  • AI algorithms are being used to analyze vast amounts of transaction data to identify patterns that might indicate fraudulent activity.
  • Benefits: This enables banks to detect fraud more quickly and accurately, reducing losses and protecting customers.
  • Challenges: Staying ahead of fraudsters who are constantly evolving their tactics is an ongoing challenge.
  1. Credit Risk Assessment:
  • AI models are being used to assess the creditworthiness of loan applicants, taking into account a wide range of factors beyond traditional credit scores.
  • Benefits: This allows banks to make more informed lending decisions, potentially expanding access to credit for underserved populations.
  • Challenges: Ensuring these models are fair and non-discriminatory is crucial.
  1. Algorithmic Trading:
  • AI is being used to analyze market trends, identify trading opportunities, and execute trades at high speed and volume.
  • Benefits: This can lead to increased profitability and improved risk management for banks involved in trading activities.
  • Challenges: The complexity and potential risks of algorithmic trading require careful oversight and regulation.
  1. Personalized Financial Advice:
  • AI-powered robo-advisors are being used to provide personalized investment advice and portfolio management services.
  • Benefits: This makes financial advice more accessible and affordable for a wider range of customers.
  • Challenges: Ensuring these robo-advisors provide suitable advice and act in the best interests of their clients is paramount.

The Road Ahead: The Future of AI in Banking

The AI revolution in banking is still in its early stages, but its potential is vast. As AI technology continues to advance, we can expect to see even more innovative and transformative applications in the years to come.

However, along with the opportunities come challenges. Ensuring the ethical and responsible use of AI, maintaining customer trust, and keeping up with the rapid pace of technological change will be key priorities for banks.

In conclusion, AI is reshaping the banking landscape, enabling institutions to operate more efficiently, serve customers better, and make more informed decisions. The banks that embrace AI and harness its power effectively are likely to be the ones that thrive in the digital age.

Douglas Davenport

A Deep Dive Into the Recent Bumpy AI Market

The initial euphoria surrounding the artificial intelligence (AI) boom seems to be waning as of late, evidenced by the recent market performance of tech giants Alphabet, Amazon, and Microsoft. These companies, once the darlings of the AI trade, have seen their shares tumble over the past month, raising questions about the sustainability of the AI-driven market rally. This article delves into the factors behind this recent downturn, exploring the interplay of market sentiment, economic indicators, and industry-specific challenges.

Market Sentiment and the AI Hype Cycle

The AI market has been on a roller coaster ride, with investor sentiment oscillating between extreme optimism and cautious skepticism. The initial surge in AI-related stocks was fueled by a wave of hype surrounding the transformative potential of AI technologies. However, as the market matures, investors are becoming more discerning, demanding concrete evidence of profitability and sustainable growth. The recent market downturn suggests that the initial AI hype cycle may be peaking, with investors reassessing their expectations and adopting a more cautious approach.

The hype cycle is a common phenomenon in emerging technologies, characterized by a period of inflated expectations followed by a trough of disillusionment. The AI market appears to be entering this trough, as investors grapple with the realities of commercializing AI technologies and navigating the complexities of the regulatory landscape. The recent market downturn may be a necessary correction, forcing investors to adopt a more realistic perspective on the AI market's growth trajectory.

Economic Indicators and Investor Confidence

The broader economic landscape also plays a crucial role in shaping investor confidence and market sentiment. Rising interest rates, inflationary pressures, and geopolitical tensions can create a climate of uncertainty, prompting investors to seek safer havens for their capital. The recent market downturn coincides with a period of economic volatility, with concerns about a potential recession looming large. These macroeconomic factors may be contributing to the decline in AI-related stocks, as investors reassess their risk appetite and prioritize stability over growth potential.

Investor confidence is a fragile commodity, easily swayed by economic indicators and market trends. The recent downturn in AI-related stocks suggests that investor confidence may be waning, as concerns about the broader economic outlook overshadow the excitement surrounding AI technologies. This shift in sentiment may be a temporary phenomenon, or it may signal a more profound reassessment of the AI market's prospects.

Industry-Specific Challenges and Market Dynamics

The AI industry faces a unique set of challenges that can impact market dynamics and investor sentiment. These challenges include:

  • Regulatory scrutiny: The rapid advancement of AI technologies has raised concerns about ethical implications, data privacy, and potential misuse. Governments around the world are grappling with the complexities of regulating AI, with the potential for stricter regulations looming on the horizon. This regulatory uncertainty can create a climate of risk aversion, deterring investors from committing capital to AI-related ventures.

  • Talent shortage: The AI industry is experiencing a severe talent shortage, with demand for skilled AI professionals far outstripping supply. This talent gap can hinder innovation, slow down product development, and increase operational costs. The scarcity of AI talent may be a limiting factor in the growth of AI-related companies, impacting their market performance and investor appeal.

  • Competition and market saturation: The AI market is becoming increasingly crowded, with numerous players vying for market share. This intensified competition can lead to price wars, margin compression, and consolidation. The risk of market saturation may be a concern for investors, as it can limit the growth potential of individual companies and create a more challenging operating environment.

  • Technological hurdles: The development and deployment of AI technologies are fraught with technical challenges, including data quality issues, algorithmic bias, and scalability limitations. These hurdles can delay product launches, increase development costs, and impact the overall user experience. The complexities of AI technology may be a source of frustration for investors, who may be seeking more tangible evidence of progress and commercial viability.

The Future of the AI Trade: Opportunities and Challenges

Despite the recent market downturn, the AI trade is far from over. The long-term potential of AI technologies remains undeniable, with numerous applications across various industries. However, the path to realizing this potential is likely to be bumpy, with challenges and setbacks along the way.

Investors who remain committed to the AI trade must adopt a long-term perspective, focusing on companies with strong fundamentals, innovative technologies, and a clear path to profitability. It is also crucial to stay informed about regulatory developments, industry trends, and macroeconomic factors that can impact the AI market.

The AI trade may be losing its luster in the short term, but the long-term outlook remains bright. The companies that can navigate the challenges and capitalize on the opportunities presented by AI technologies are likely to emerge as the winners in this evolving market landscape.

Additional Insights and Considerations

  • The recent downturn in AI-related stocks may be a healthy correction, allowing the market to recalibrate and realign expectations with reality.
  • Investors should focus on companies with a proven track record of innovation, a strong management team, and a clear strategy for commercializing AI technologies.
  • The regulatory landscape is evolving rapidly, and investors should stay informed about potential changes that could impact the AI market.
  • The talent shortage in the AI industry is a significant challenge, and companies that can attract and retain top talent are likely to have a competitive advantage.
  • The AI market is becoming increasingly competitive, and companies must differentiate themselves through innovation, customer focus, and operational efficiency.
  • The long-term potential of AI technologies remains vast, and investors who can navigate the challenges and capitalize on the opportunities are likely to reap significant rewards.

Conclusion

The AI trade has undoubtedly lost some of its luster in recent weeks, as the market grapples with a confluence of factors, including shifting investor sentiment, economic uncertainty, and industry-specific challenges. However, the long-term potential of AI technologies remains undeniable, and the companies that can navigate the complexities of this evolving market are likely to emerge as the leaders of tomorrow. Investors who remain committed to the AI trade must adopt a long-term perspective, focus on fundamentals, and stay informed about the latest developments in this dynamic and rapidly changing field.

The AI revolution is far from over, and the opportunities for growth and innovation remain abundant for those who are willing to embrace the challenges and seize the moment.

Douglas Davenport

TEXTBOOK DISRUPTION

(PSO), (IBM), (CHGG), (LPL), (NVDA), (AMZN)

What if textbooks could read you as well as you read them? That's the promise of AI-powered learning. 

But what exactly does this AI-powered future look like?

First, forget those dusty, static tomes of your school days. Instead, get with the times and think of textbooks that learn with you, adjusting their content and pace to match your individual needs. 

They'll pinpoint your strengths and weaknesses faster than a Wall Street analyst spotting a market trend, and deliver personalized lessons and feedback in real-time.

These digital marvels will assess a student's grasp of calculus quicker than you can say "derivative," serving up personalized content faster than a short-order cook on amphetamines. 

We're talking real-time feedback, custom-tailored lessons, and engagement levels that'll make social media look boring. 

For the class dunce, it's extra help. For the whiz kid, it's brain fuel. 

Basically, using AI textbooks is like having a tutor that never sleeps, doesn't charge by the hour, and won't judge you for showing up in your pajamas.

And trust me, this isn't some pie-in-the-sky idea that only appeals to educators. In fact, it's already shaping up to be a multi-billion dollar goldmine for sharp-eyed investors.

Let's start with the big picture. The broader education technology (EdTech) market is on track to explode to a mind-boggling $404 billion by 2025. 

We're not talking about a gentle uptick here. This is the kind of growth that makes the Gold Rush look like a garage sale. 

Zoom in a bit, and you'll see the “AI in Education” market is estimated to grow at a jaw-dropping CAGR of 47% from 2021 to 2027, reaching $3.68 billion by 2023. 

And the global textbook market? It's set to hit a cool $20.6 billion by 2026, growing at a steady 3.4% CAGR from 2021. 

But here's the kicker: AI-powered textbooks are poised to disrupt this entire industry faster than you can say "digital transformation."

Don't just take my word for it, though. Look at what's already happening in the real world. 

Carnegie Learning's MATHia uses AI to make math less painful than a root canal. DreamBox Learning adapts to students faster than a chameleon on a disco floor. 

And Duolingo? They're making language learning more addictive than your favorite smartphone game. 

Needless to say, AI isn’t just about cool tech toys anymore. It’s also reshaping how we learn. 

Studies show these interactive, personalized experiences can boost student engagement by up to 60%. 

And it's not just the tech nerds who are excited. A survey by EdWeek Research Center found that 66% of teachers believe AI can personalize learning for students. 

When was the last time 66% of teachers agreed on anything? 

Meanwhile, venture capitalists, those professional gamblers of the finance world, poured a record $16.1 billion into EdTech in 2020. 

That's not just putting your money where your mouth is—it's stuffing your entire wallet down your throat and asking for seconds.

So, who are the players in this educational gold rush? Let's start with the old guard. 

Pearson PLC (PSO) is using AI for everything from adaptive learning to automated grading. They’re looking to partner with IBM (IBM) and its Watson AI, creating a brain trust that would make Einstein look like a slack-jawed yokel. 

Chegg (CHGG) is another one to watch. It uses AI to solve problems and catch plagiarists quicker than a frat house can empty a keg.

But it's not just the education-specific players making moves. 

Tech giants are stepping into the classroom. LG Electronics (LPL) is expanding into the education sector faster than a teenager's excuse for missing homework. 

NVIDIA (NVDA) is providing the raw computing power, Amazon (AMZN) is offering the cloud infrastructure, and IBM is bringing its AI expertise to the table. 

It's like the Avengers of AI, but instead of saving the world, they're trying to make sure little Timmy finally grasps fractions.

And the future of edtech? It's so bright, you might need to wear shades in the classroom. 

We're talking VR field trips to ancient Rome, AI tutors that make Socrates look like a slacker, and learning analytics that can predict academic success better than a helicopter parent. 

I know that for some of us, passing notes was high-tech classroom communication. Obviously, everything we know about school has changed since then. 

Now, AI is writing the whole textbook. I suggest you start studying this sector now, or risk being left back a grade in the school of profitable opportunities. 

Douglas Davenport

FOLLOW THE YELLOW CHIP ROAD

(REGN), (MRNA), (BLK), (GS), (LMT), (NOC), (IBM), (GOOGL), (TRMB), (TSLA), (GM), (FDX), (UPS), (MSFT), (NVDA), (AI), (PLTR)

"In the world of AI, we're not in Kansas anymore, Toto," I muttered to myself as I stared at the latest headlines about OpenAI's Project Strawberry. Buckle up, Dorothy, because if you thought AI was already shaking things up, we're about to take a technicolor trip into a world where machines don't just crunch numbers - they reason like Einstein on steroids.

Sam Altman and OpenAI are teasing us with "strawberries" again. But this isn't farmer's market gossip. 

We're talking about Project Strawberry, or Q* as the insiders call it, OpenAI's shot at artificial general intelligence (AGI) – AI that could outperform humans in most economically valuable tasks. 

In fact, some OpenAI employees are so confident, they're betting their bottom dollar that Q* could be the AGI breakthrough we've all been waiting for.

Now, here's where it gets as exciting as finding a bull market in a sea of bears. Q* has been flexing its mathematical muscles, solving problems at a grade-school level.  

You might think, "Big deal, my calculator can do that." But hold your horses. This isn't just about crunching numbers; it's about reasoning. 

And that's where the investment landscape starts to shift dramatically. While your run-of-the-mill AI is busy predicting the next word in a sentence, Q* is laying the groundwork for revolutionizing scientific research and God knows what else.

Let's explore how some of the major players could harness this potential.

Imagine Regeneron Pharmaceuticals (REGN) and Moderna (MRNA) using this tech to turbocharge drug discovery. We could be looking at new wonder drugs hitting the market faster than you can say "artificial intelligence." 

Or picture BlackRock (BLK) and Goldman Sachs (GS) wielding Q*'s mathematical prowess to create financial models so sophisticated they make Warren Buffett look like he's using an abacus.

And it doesn’t end there. Lockheed Martin (LMT) and Northrop Grumman (NOC) could use Q*'s brainpower to calculate missile trajectories that would make NASA green with envy. 

Let's not forget IBM (IBM) and our old friend Alphabet (GOOGL) - they're betting big on quantum computing, and Q* could be the secret sauce that makes those quantum bits dance.

And if climate change got you down? Companies like Trimble (TRMB) could harness Q*'s capabilities to model climate patterns so accurately you'll know whether to pack sunscreen or an umbrella for your vacation next year. 

As for all you Tesla (TSLA) and General Motors (GM) bulls out there, imagine self-driving cars with the reasoning power of a math genius. Your morning commute could become smoother than a well-aged scotch.

Even logistics giants like FedEx (FDX) and UPS (UPS) could use Q*'s mathematical magic to optimize their supply chains. Your packages might start arriving before you even realize you need them.

But we can't talk about AI without giving credit to the major tech players who are pushing it forward. 

Microsoft (MSFT) is sitting pretty as OpenAI's sugar daddy. They've already plugged OpenAI's tech into Azure, and in Q2 2023, their AI-driven revenue in Azure shot up faster than a SpaceX rocket - we're talking 150% year-over-year growth. 

If Project Strawberry delivers the goods, Microsoft will be first in line at the AI buffet.

Then there's Nvidia (NVDA), the company making the brains that power these AI behemoths. In Q3 2023, their data center revenue, fueled by AI hunger, exploded to $14.51 billion - that's a 279% year-over-year jump, folks. 

As Project Strawberry and its AI cousins evolve, Nvidia's hardware will be hotter than beach sand in July.

Don't count out Alphabet either. While they're not in bed with OpenAI, they're no slouch in the AI department. Their Google Cloud platform saw revenue grow 22% year-over-year to $8.4 billion in Q3 2023. 

As this AI arms race heats up, expect Alphabet to double down on their AGI efforts faster than you can say "Hey Google."

For those of you with a taste for smaller players, keep an eye on C3.ai (AI). These folks are serving up enterprise AI solutions like hotcakes at a lumberjack convention.

In Q2 fiscal 2024, they reported revenue of $73.2 million, up 17% year-over-year. If Project Strawberry proves its mettle, C3.ai could be swamped with clients faster than a trendy restaurant on Valentine's Day.

And let's not overlook Palantir Technologies (PLTR). Their big data analytics could get a serious boost from advancements in AI reasoning. 

They reported Q3 2023 revenue of $558 million, up 17% year-over-year. If Project Strawberry's math skills translate to better data crunching, Palantir could be offering insights sharper than a samurai's sword.

With the global AI market projected to hit $407 billion by 2027, the AI revolution is here, serving up opportunities juicier than a prime rib at Peter Luger's. 

As I said, we're not in Kansas anymore. Project Strawberry and its kind promise a world where AI doesn't just process, it ponders. 

It's a brave new world, and it'll take more than ruby slippers to navigate. But for those willing to embrace the yellow brick road of innovation, the emerald city of profit awaits. 

Just remember, in this Oz of AI, keep your wits sharp, your courage strong, and your heart big. Because in the end, there's no place like a well-informed portfolio. Now, if you'll excuse me, I've got some flying monkeys - I mean, market trends - to watch.

Douglas Davenport

THE JETSONS WERE RIGHT

(EADSY), (BA), (AAL), (DAL), (UAL), (ACHR), (EVEX), (JOBY), (LILM), (EVTL), (GOOG), (TSLA), (AI), (LAZR), (CPTN), (INVZ), (MBLY), (NRNC), (STLA), (DAL), (NVDA)

It looks like the Jetsons' future is finally landing in our backyard. Minnesota just became the second U.S. state to roll out the welcome mat for flying cars, following New Hampshire's lead back in 2020. 

They're calling it the "Jetsons Law," and it's got me more excited than a kid with a new remote-control airplane.

Now, before you start plotting your airborne commute to beat the morning traffic, let's pump the brakes (or should I say, adjust the flaps?). We're still a few years away from skyways replacing highways, but the regulatory runway is being paved as we speak.

This new law in Minnesota is letting folks register their "roadable aircraft" as motor vehicles, using tail numbers instead of license plates. It's like your car and your private jet had a baby, and the government is finally acknowledging its existence. 

But don't get any ideas about taking off from your driveway – these flying cars still need to use designated airfields for takeoffs and landings. The FAA isn't ready to see Ford Focuses sprouting wings just yet.

Now, you might be asking, "Who's actually building these things?"

Well, we've got the old guard of aviation, Airbus (EADSY) and Boeing (BA), throwing their considerable weight behind flying taxis and eVTOL (that's "electric vertical takeoff and landing" for those of you who don't speak aerospace) prototypes. 

Not to be outdone, the airlines are getting in on the action too. American Airlines (AAL), Delta (DAL), and United (UAL) are all pouring millions into this pie-in-the-sky idea.

But the real action is with the new kids on the block. 

We're talking companies like Archer Aviation (ACHR), Eve Air Mobility (EVEX), Joby Aviation (JOBY), Lilium (LILM), and Vertical Aerospace (EVTL). 

These upstarts are betting the farm on urban air mobility, and let me tell you, the numbers they're throwing around are enough to make your head spin faster than a helicopter rotor.

Take Archer Aviation, for example. These folks are so confident in the future of urban air mobility that they're projecting a market worth $1 trillion by 2030. 

To put that in perspective, that's about the same as the entire economy of Mexico. I'm not saying we should all sell our cars and invest in personal helicopters just yet, but those are some eye-popping figures.

Interestingly, most car manufacturers are sitting this one out. They're more focused on self-driving cars and electric vehicles. It's like they're saying, "Let's master the road before we conquer the skies." 

Smart move or missed opportunity? Only time will tell.

Now, here's where it gets really interesting. AI is set to play a bigger role in these flying cars than a backseat driver on a family road trip. We're talking autonomous flight, predictive maintenance, and traffic management systems that would make air traffic controllers obsolete.

Companies like Alphabet (GOOG) (through its Waymo subsidiary) and Tesla (TSLA) are already knee-deep in self-driving tech. It's not a huge leap to imagine that same tech guiding your flying car safely through the skies. 

And when it comes to keeping these sky-high jalopies in tip-top shape, AI companies like C3.ai (AI) and Uptake Technologies are chomping at the bit to apply their predictive maintenance magic.

But let's not forget about the elephant in the room – or should I say, the jetpack in the garage? Safety. 

We're talking about vehicles that need to be roadworthy and airworthy. It's like asking your SUV to also be a submarine. That's where companies like Luminar Technologies (LAZR), Cepton (CPTN), and Innoviz Technologies (INVZ) come in. 

These folks are working on lidar sensors that could help your flying car avoid collisions, whether you're cruising down Main Street or soaring over it.

And let's not overlook the brains of the operation. Mobileye (MBLY) and Cerence Inc. (CRNC) are cooking up AI systems that could make piloting a flying car as easy as asking Siri for directions. 

Imagine telling your car, "Take me to work," and then sitting back to enjoy your coffee while it handles the rest. That's the kind of future we're looking at.

Now, I know what you're thinking. "John, this all sounds great, but how do I get a piece of this airborne action?" Well, here's where it gets tricky. 

The flying car industry is still in its infancy. It's like trying to invest in smartphones back when the Motorola brick was cutting-edge technology.

But if you're looking to dip your toes in these high-flying waters, companies like Archer Aviation and Joby Aviation are good places to start. They're making real progress in eVTOL technology and have partnerships with big names like Stellantis (STLA) and Delta Air Lines (DAL), respectively.

For those of you who prefer a more grounded approach (pun absolutely intended), keep an eye on companies developing the underlying technologies. 

Nvidia (NVDA), for instance, isn't building flying cars, but their AI chips could very well be the brains behind them.

Remember, though, this is a long-term play. We're talking years, maybe even decades, before flying cars become as common as Ubers.

But for those with patience and a high tolerance for risk, the potential rewards could be sky-high.

In the meantime, I'll be keeping my feet firmly on the ground and my eyes on the horizon. After all, in the world of investing, sometimes the biggest gains come from spotting the next big thing before it takes off. 

And in this case, that takeoff might just be literal.

So, while I'm excited about the potential, I'm not rushing to sell my car just yet. I'll be watching this space closely, ready to jump in when the time is right. 

Because in investing, as in aviation, timing is everything.

 

Douglas Davenport

Nvidia's Blackwell B200 Chip Delayed Due to Design Flaw: A Setback in the AI Race

Nvidia, the world leader in graphics processing units (GPUs), has hit a significant roadblock in its highly anticipated Blackwell B200 chip launch. A design flaw, discovered late in the production process, has forced the company to delay the release of these powerful chips, expected to be a game-changer in the artificial intelligence (AI) landscape. This unexpected delay could have ripple effects throughout the tech industry, particularly for companies heavily invested in AI development and those relying on Nvidia's hardware to power their AI initiatives.  

The Blackwell B200: A Promise of AI Power

The Blackwell architecture, Nvidia's next-generation GPU design, was poised to revolutionize AI computing. The B200, the flagship chip in this series, promised to deliver unprecedented performance and efficiency for AI workloads, such as training large language models and powering complex AI applications. The chip's advanced features, including a massive increase in processing power, improved memory bandwidth, and enhanced energy efficiency, made it a highly sought-after component for data centers and AI researchers worldwide.  

Nvidia's partners, including tech giants like Microsoft, Meta, and Google, had eagerly awaited the B200's arrival, hoping to leverage its capabilities to accelerate their AI projects and gain a competitive edge in the rapidly evolving AI landscape. The chip's delay, therefore, has come as a major disappointment, leaving these companies scrambling to adjust their plans and potentially delaying their own AI initiatives.

The Design Flaw: A Late Discovery

The design flaw, reportedly discovered by Nvidia's manufacturing partner Taiwan Semiconductor Manufacturing Company (TSMC), affects the processor die connecting two Blackwell GPUs on a single board. This critical component, responsible for communication and data transfer between the GPUs, was found to have a defect that could impact the chip's performance and reliability.  

The late discovery of this flaw, unusually late in the production process, has raised concerns about Nvidia's quality control and testing procedures. The company typically conducts rigorous testing throughout the chip development cycle to identify and address any potential issues before mass production. However, in this case, the flaw managed to slip through the cracks, resulting in a costly and embarrassing delay.  

Consequences of the Delay

The delay of the B200 chip is expected to have significant ramifications for Nvidia and the broader tech industry. For Nvidia, the delay could impact its financial performance, as the company had projected strong sales of the B200 to its major partners. The setback could also tarnish Nvidia's reputation as a reliable provider of cutting-edge AI hardware, potentially opening the door for competitors like AMD to gain market share.  

For Nvidia's partners, the delay could disrupt their AI development timelines and force them to reconsider their hardware choices. Some companies may opt to wait for the B200 to become available, while others may explore alternative solutions from other vendors. This could create opportunities for AMD and other GPU manufacturers to capitalize on Nvidia's misstep and attract new customers.

The delay could also slow down the pace of AI innovation, as researchers and developers who were counting on the B200's capabilities may have to scale back their ambitions or delay their projects. This could have a ripple effect on various industries that are increasingly relying on AI to drive growth and efficiency, such as healthcare, finance, and transportation.

Nvidia's Response

Nvidia has acknowledged the design flaw and is working to rectify the issue. The company has stated that it is revising the chip's design and will conduct further testing with TSMC before resuming mass production. Nvidia has also assured its partners that it is committed to delivering the B200 as soon as possible, but the revised timeline now extends into 2025.  

Looking Ahead

The delay of the Blackwell B200 chip is a significant setback for Nvidia, but it is not necessarily a fatal blow. The company has a strong track record of innovation and has overcome challenges in the past. However, the incident serves as a reminder that even industry leaders are not immune to mistakes and that the development of complex technology like GPUs is fraught with risks.

As Nvidia works to address the design flaw and resume production of the B200, the AI community will be watching closely. The chip's success or failure could have a major impact on the trajectory of AI development and the competitive landscape in the GPU market.

 

Douglas Davenport

CURB YOUR ENTHUSIASM

(SERV), (NVDA), (UBER), (DASH)

Well, it looks like Nvidia's (NVDA) not content with just ruling the AI chip roost. They've decided to take a stroll down Robotics Lane, and boy, is it turning heads.

Remember when I told you about the barbell strategy, balancing tech and recovery stocks? Well, Nvidia's playing its own version of financial Twister, with one foot firmly planted in AI chips and the other testing the waters of autonomous delivery.

The chip giant just converted a promissory note faster than you can say "burrito delivery," snagging over a million shares in Serve Robotics (SERV). 

This brings Nvidia's total investment to l $12 million, giving it a 10% stake in the company. And, when the news broke, Serve's stock shot up 225%.

But let's rewind a bit. Serve actually has quite the pedigree. 

Serve Robotics is the wonder child of the delivery world. It started life as Postmates X, the robotic brainchild of Postmates. But in 2020, Uber (UBER) crashed the family reunion, adopting Postmates for a cool $2.65 billion. 

In the ensuing chaos, little Serve was spun out faster than you can say “emancipation,” becoming the independent robot delivery wunderkind we know today. 

At Serve's public debut, Nvidia already owned 2.614 million shares. They then snagged another 62,500 shares in a private placement. 

This latest move adds another 1.050 million shares to their collection, bringing the total to 3.727 million. 

Now, Serve's little R2D2s aren't just eye candy. They've been zipping around Los Angeles since 2020, completing over 10,000 deliveries for Postmates by year's end. 

Fast forward to 2023, and they've expanded to a fleet of 100 robots, completing over 50,000 deliveries for 300 restaurants with a jaw-dropping 99.94% success rate. 

That's more reliable than my Swiss watch - and trust me, that thing's outlasted relationships.

These sidewalk warriors boast Level 4 autonomy, meaning they can navigate sidewalks using AI without human intervention. It's like giving a Roomba a promotion and a delivery bag.

The company's got big plans as well, aiming to deploy 2,000 of these mechanical meal couriers by 2025 in cities like San Diego, Dallas, and Vancouver. 

They've even got Magna International (MGA), a $12 billion auto parts bigwig, signed on to manufacture these robots exclusively.

But let's zoom out for a second. Serve's management is betting on a global market for robotic and drone delivery worth a staggering $450 billion in annual revenue by 2030. 

That's no small potatoes. Just look at DoorDash (DASH), which saw its revenue skyrocket by 200% from 2020 to 2023.

The US food delivery industry alone is set to generate over $353 billion in revenue this year. DoorDash leads the pack with a 67% market share, followed by Uber Eats at 23%. 

Both rely on human drivers, but Serve is asking the million-dollar question: Why use a 2-ton car to deliver a 2-pound burrito?

Now, you might be thinking, "John, this sounds like the next big thing." And you might be right... eventually. 

But before you rush to jump on this robotic bandwagon, let's take a closer look at what we're dealing with here.

Serve's revenue last year was a modest $207,545 - less than I spend on vintage wine. Sure, they're growing. Q1 2024 saw them rake in $946,711, but $850,000 of that was from the Magna licensing deal. 

Their actual delivery business though? It's pulling in about as much as a lemonade stand in a desert.

And here's where it gets dicey: Serve is burning through cash faster than a sailor on shore leave. They lost $9 million in Q1 2024 alone, with $8.3 million in operating costs. 

At this rate, they're on track to lose significantly more than the $20.7 million they hemorrhaged in 2023.

But hey, they've got some heavy hitters in their corner. Besides Nvidia's $12 million investment since 2018, Uber has thrown in $11.5 million. Even Delivery Hero (DLVHF) and 7-Eleven are getting a piece of the action.

Now, don't get me wrong. I love new tech as much as the next guy. Heck, I've flown MiG-25s at the edge of space and climbed Everest. But even I know that sometimes, the view from the top isn't worth the climb if your gear's not up to snuff.

Sure, Nvidia's backing is a nice vote of confidence. But let's put this in perspective: Nvidia's $12 million investment is like me losing the change in my couch cushions. It's a rounding error for a $2.9 trillion behemoth.

What's the play here then? Well, as much as I'd love to tell you to go all-in on robot deliveries, I'm going to have to curb your enthusiasm (get it?) and pour some cold sake on that idea.

The robot revolution in last-mile delivery is coming, no doubt about it. But at the moment, investing in Serve is like trying to deliver a souffle by drone - it might work eventually, but there's a good chance it'll end up a mess on your doorstep.

So, for now, Serve Robotics is a "watch and wait" situation.

 

Douglas Davenport

AI’S GREATEST HITS

(GOOGL), (SPOT), (UMGNF), (NVDA), (MSFT), (SSTK)

It's time to face the music. AI is composing tunes, and it's not just a passing note in the tech symphony. This could be the next big score for us investors and the entertainment industry.

Let's lay down some beats here. The global market for AI-generated music is set to hit a high note of $3 billion by 2028. That's a tenfold jump from 2023. 

And it's not just a solo performance. The whole AI in music market is expected to crescendo to $1.6 billion by 2025, growing at a CAGR of 27% from 2020. 

Now, who's leading this AI orchestra? 

For starters, we've got Alphabet Inc. (GOOGL) and its brainy offspring, DeepMind. They're not just whistling Dixie with their WaveNet tech. With a war chest of $110.9 billion as of Q4 2023, Alphabet's got plenty of cash to keep the music flowing.

Then there's Spotify Technology S.A. (SPOT). With 489 million monthly active users tapping their feet to its tunes in Q4 2023, Spotify's not just sitting on the sidelines. They're exploring AI-generated music faster than you can say "playlist." 

And they're not alone - 60% of major music streaming platforms are already using AI to personalize your listening experience.

But what about the old guard? Universal Music Group (UMGNF) isn't about to let AI steal the show. With revenues of $10.65 in 2023, they're partnering up with AI music startups faster than you can say "collab." Smart move, if you ask me.

And let's not forget the hardware guys. As expected, there’s NVIDIA Corporation (NVDA), which has been a key player in this AI music revolution. 

Their chips are the backstage crew making it all happen. With data center revenue jumping 41% year over year to $15.7 billion in fiscal 2024, NVIDIA is looking at an encore performance.

The money men are taking notice, too. Between 2019 and 2023, venture capitalists pumped $1.37 billion into music-related AI startups. 

OpenAI's MuseNet scored a cool billion from Microsoft (MSFT) and friends, while Aiva Technologies hit the right note with $10 million in Series A funding in 2022.

We're also seeing some interesting duets in the M&A world. For example, Shutterstock's (SSTK) buyout of Amper Music in 2020 shows they're not just about pretty pictures anymore.

So, how are these AI maestros making their dough? Well, it's a mixed playlist. 

Licensing fees are the chart-topper at 40% of revenue, with subscription services humming along at 30%. Those monthly subs? They'll set you back $10 to $30. 

Streaming service partnerships chip in another 20%, with deals ranging from $500,000 to $2 million a year. Not a bad gig if you can get it.

But AI isn't just composing. It's mastering tracks, running marketing campaigns, and even playing DJ with personalized playlists. 

Video game soundtracks? 15% of those released in 2023 had AI fingerprints all over them. And 25% of ad agencies are now using AI to cook up jingles and background scores.

And here's the kicker - people are actually digging it. A whopping 80% of consumers are cool with listening to AI-composed tunes, and half of them can't even tell the difference from human-made music in blind tests. 

Even more surprising? 65% of listeners think AI can actually boost human creativity. Talk about harmony.

But it's not all smooth sailing. Reports indicate a potential 27% drop in human music creators' revenues by 2028 if we don't figure out how to pay them for their input. 

That means we need to get our act together on copyright laws and fair compensation, pronto.

Looking ahead, the AI-music mashup is just warming up. These digital Mozart's can now churn out a full symphony in under 4 hours, down from 24. 

And they can mimic over 500 composers across all sorts of genres. It's like having a time machine for music.

For those of you with an ear to the ground, this AI-composed symphony could be music to their portfolios. 

As this tech hits its stride and gains more fans, it's a chance to get in on the ground floor of a revolution in one of humanity's oldest art forms.

So, what's the takeaway here? Well, I say keep your eyes on the AI music scene. It's not just noise - it's the sound of opportunity knocking. 

And who knows? The next chart-topping hit might just come from a silicon chip instead of a recording studio. 

Now that's something to sing about.

Legal Disclaimer

There is a very high degree of risk involved in trading. Past results are not indicative of future returns. MadHedgeFundTrader.com and all individuals affiliated with this site assume no responsibilities for your trading and investment results. The indicators, strategies, columns, articles and all other features are for educational purposes only and should not be construed as investment advice. Information for futures trading observations are obtained from sources believed to be reliable, but we do not warrant its completeness or accuracy, or warrant any results from the use of the information. Your use of the trading observations is entirely at your own risk and it is your sole responsibility to evaluate the accuracy, completeness and usefulness of the information. You must assess the risk of any trade with your broker and make your own independent decisions regarding any securities mentioned herein. Affiliates of MadHedgeFundTrader.com may have a position or effect transactions in the securities described herein (or options thereon) and/or otherwise employ trading strategies that may be consistent or inconsistent with the provided strategies.