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Demystifying the Giants: Llama 3 70B and GPT-4 in the AI Landscape

The arena of Artificial Intelligence (AI) is witnessing a thrilling race between the newer open-source Llama 3 70B model by Meta and OpenAI’s proprietary GPT-4 model. Each model has its forte, and a deep dive into their comparative performance can provide valuable insights for developers, researchers, and businesses looking to leverage AI.

Performance Showdown

GPT-4 has been turning heads with its ability to process static visual inputs, making it a lone warrior in multimodal AI tasks among the discussed models. This unique feature widens its application scope, allowing it to handle tasks that blend images with text. However, size doesn’t always equate to speed. Llama 3 70B, with its smaller size compared to GPT-4, takes the lead in terms of speed and efficiency, making it a better choice for projects where these factors are critical (Neoteric).

For specialized tasks like coding, Llama 3 70B showcases its prowess, with Meta AI researchers suggesting that Code Llama capacities are sufficient even for complex tasks like mapping ambiguous specifications to code (33rd Square). However, GPT-4 is no slouch; it boasts top-tier performance across various human-centric exams, demonstrating broad capabilities (33rd Square).

Open Source vs. Proprietary: Implications

Llama 3 70B, being open-source, offers broad accessibility and collaborative improvement opportunities. It stands as a testament to the democratization of AI, providing a foundation for innovative applications without the hefty price tag of big players like OpenAI and Google (33rd Square). On the flip side, GPT-4’s closed-source nature offers businesses a competitive advantage with its proprietary technology, though at a potential cost to flexibility and experimentation (Codesmith.io).

Cost and Accessibility

When it comes to cost, Llama 3 70B stands out for its affordability. The open-source model allows for significant cost savings, particularly when it comes to summarization tasks, offering a cost-effective alternative to GPT-4 while maintaining comparable accuracy (Anyscale, Prompt Engineering). This cost efficiency does not imply a compromise in quality, as Llama 3 70B has shown near-human levels of performance in spotting factual inconsistencies (Anyscale).

Ethical Considerations

The advancement of AI comes with its share of ethical challenges, including concerns around security, integrity, and bias. OpenAI has invested heavily in safety engineering, aiming to develop general intelligence responsibly. In comparison, the specialization of Llama 3 70B naturally constrains some of the risks associated with more generalized models (33rd Square).


How to Install and Run Llama3 on a MacBook Pro

Introduction
This will guide you through the process of installing and running the Llama3 model on a MacBook Pro. This is particularly useful for developers and researchers interested in machine learning model inference.

Step 1: Install Homebrew and wget

  • Open Terminal.
  • Install Homebrew by running:
    /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  • Install wget with Homebrew:
    brew install wget

Step 2: Download Llama3

  • Use the
    download.sh
    script provided by Llama3’s repository to download the models. Make sure to give executable permissions to the script if it doesn’t already have them:
    chmod +x /Users/your-username/Downloads/download.sh
  • Run the script:
    /Users/your-username/Downloads/download.sh

Step 3: Install and Setup Llama3

  • Follow the instructions on Llama3’s GitHub repository to set up the environment. This usually involves cloning the repository and setting up Python dependencies.

Step 4: Convert and Quantize the Model

  • If required, convert the Llama3 model to a compatible format and quantize it to improve performance. Commands for this will be available in the Llama3 documentation.

Step 5: Run the Model

  • Execute the model using the command specified in the documentation, adjusting parameters as necessary for your application.

Conclusion
Installing and running Llama3 on a MacBook Pro can be straightforward with the right tools and instructions. This setup enables you to perform machine learning tasks efficiently right from your local environment.

For detailed commands and more specific setup options, always refer to the official Llama3 GitHub page.

Conclusion: The AI Crossroads

Both Llama 3 70B and GPT-4 present compelling cases for different applications. Llama 3 70B’s open-source model and cost efficiency make it an attractive option for businesses looking to scale high-quality AI tasks affordably. GPT-4, with its expansive capabilities, including image processing, remains a robust choice for complex and creative AI applications.

LLaMA 3 70B and GPT-4 represent significant milestones in AI development, each with its own set of strengths, use cases, and implications for the future of AI. As these models continue to evolve, they will likely define new possibilities for AI’s role in various industries, from programming to content creation to customer service. The choice between using an open-source model like LLaMA 3 70B or a proprietary model like GPT-4 may come down to individual needs, resources, and objectives, as well as the value placed on community involvement and cost considerations.

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Revolutionizing Risk Management in Finance with AI

The integration of Artificial Intelligence into risk management within the financial services sector is not just a trend; it’s a seismic shift towards more secure, efficient, and intelligent operations. Financial institutions are increasingly turning to AI to navigate the complexities of modern finance, from predicting market movements to safeguarding against fraud.

Navigating the AI Landscape in Risk Management

AI’s role in risk management spans various applications, each contributing uniquely to fortifying financial operations against contemporary challenges. Here’s how AI is transforming risk management:

AI for Enhanced Risk Detection and Management

Financial institutions are employing AI for its predictive capabilities and data analytics prowess, enabling them to make more informed decisions. AI’s ability to analyze vast datasets at unprecedented speeds helps in identifying patterns that humans might overlook, thereby predicting potential market shifts or identifying fraudulent activities with higher accuracy (KPMG).

AI technologies like machine learning (ML) and deep learning are at the forefront, offering sophisticated models for stress testing and credit risk modeling. These technologies provide superior forecasting accuracy by capturing nonlinear effects between variables, optimized variable selection for risk models, and richer data segmentation for enhanced modeling accuracy (KPMG).

Specific Use Cases in Banking

  1. Fraud Detection: AI’s real-time analysis capabilities are pivotal for detecting and preventing fraud. ML models trained on vast amounts of transaction data can swiftly identify anomalies, reducing the incidence of credit card fraud and enhancing transaction security (Inscribe).
  2. Regulatory Compliance Management: Compliance with national and international regulations is streamlined with AI. Machine learning automates the review of large data sets, ensuring efficient and accurate compliance activities, thus saving significant costs and reducing the likelihood of penalties (Inscribe).
  3. Liquidity and Cybersecurity Risk Management: AI assists banks in ensuring sufficient liquidity to meet customer demands and in identifying vulnerabilities across data points to mitigate cyberattacks, thus protecting both customer assets and the institution’s integrity (Inscribe).

Strategies for Implementing AI in Risk Management

Adopting AI in risk management necessitates a strategic approach. Financial services firms must assess the implications of AI on their business models and the impact of regulatory requirements on AI adoption. Regulatory scrutiny on AI has intensified, focusing on systemic and long-term risks such as market resilience and the control over AI systems (PwC). Firms should adopt a comprehensive approach to effectively manage risks and leverage AI’s potential.

Prioritizing AI Implementation

  1. Assess the Landscape: Understand the specific risks your institution faces and how AI can address these. Consider AI’s potential to enhance decision-making and operational efficiency.
  2. Regulatory Compliance: Keep abreast of evolving AI regulations to ensure compliance. Develop robust governance structures around AI use to address regulatory expectations (Deloitte).
  3. Partnership with AI Experts: Collaborating with AI experts and vendors can provide the necessary technical expertise and insights into best practices in AI risk management.
  4. Employee Training: Equip your workforce with the knowledge to understand and work alongside AI technologies, emphasizing the ethical and responsible use of AI.

Customer Service Transformation with AI

AI’s impact extends beyond risk management, revolutionizing customer service in the financial sector. AI-powered chatbots and virtual assistants provide personalized customer interactions, offering quick responses to inquiries and facilitating transactions, thereby enhancing the customer experience. This not only boosts customer satisfaction but also allows financial institutions to deploy human resources to more complex tasks, optimizing overall efficiency.

Moving Forward

The future of finance with AI looks promising, with the technology set to redefine risk management and customer service. Financial institutions that strategically adopt and adapt to AI stand to gain a competitive edge in the rapidly evolving financial landscape.

For a deeper dive into how AI is transforming risk management and customer service in finance, consider exploring resources from industry experts like Deloitte, PwC, KPMG, and Inscribe.

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Harnessing AI Power in Finance: A Deep Dive into Gemini, ChatGPT, Claude, and Beyond

The financial industry stands on the brink of a transformative era powered by AI. Google’s Gemini, OpenAI’s ChatGPT, Anthropic’s Claude, and a plethora of other generative AI tools are redefining how companies in the financial sector innovate, streamline operations, and engage with customers. Let’s embark on an exploratory journey to understand these AI marvels and their potential impact on the financial landscape.

Navigating the AI Landscape in Finance

1. Google’s Gemini: The Multimodal Maestro

Google’s latest AI prodigy, Gemini, has made waves with its unparalleled capabilities. Engineered to excel in tasks ranging from commonsense reasoning to sophisticated coding challenges, Gemini 1.0 Ultra has set new benchmarks in AI performance​​. With versions like Ultra, Pro, and Nano, Gemini caters to diverse needs, from heavy-duty data analysis to mobile device applications​​. Its integration into Google’s ecosystem, including products like Bard and services offered through Google Cloud Platform, Vertex AI, and AI Studio, offers a seamless AI experience for developers and businesses alike​​​​.

2. OpenAI’s ChatGPT: The Conversational Wizard

ChatGPT, renowned for its conversational prowess, has captivated users with its ability to generate human-like text responses. While primarily text-based, its applications in coding and various domains highlight its versatility. However, compared to Gemini’s advanced multimodal capabilities, ChatGPT primarily relies on text, with image processing supported through additional models​​.

3. Anthropic’s Claude: The Ethical AI Companion

Claude, by Anthropic, emphasizes safety and ethical AI usage. Its design prioritizes understanding and mitigating AI’s societal impacts. While specifics on Claude’s capabilities in the financial domain are less documented, its ethical framework suggests a focus on responsible AI applications​​.

4. The Unsung Heroes: Lesser-Known Generative AI Tools

Beyond the headliners, Google Cloud Platform hosts a variety of AI tools that promise significant benefits for financial services. These include specialized models and APIs designed for tasks like natural language processing, data analytics, and customer interaction automation. These tools offer financial institutions opportunities for innovation, risk management, and enhanced customer service​​.

Transforming Finance with AI: Practical Applications

  1. Customer Service Automation: Leveraging AI for personalized customer interactions, from banking chatbots to investment advice.
  2. Risk Assessment and Management: Utilizing AI models to analyze market trends, assess credit risks, and detect fraudulent activities.
  3. Operational Efficiency: Automating routine tasks, optimizing algorithmic trading strategies, and streamlining regulatory compliance processes.
  4. Innovative Product Development: Creating AI-driven financial products that adapt to customer needs and market conditions in real-time.

Embracing the AI Revolution in Finance

The introduction of models like Gemini, ChatGPT, and Claude, along with Google Cloud’s suite of AI tools, marks a significant milestone in the financial industry’s journey towards digital transformation. These technologies not only promise to enhance operational efficiencies but also pave the way for more personalized, secure, and innovative financial services.

Financial institutions that embrace these AI advancements stand to gain a competitive edge through improved customer experiences, more accurate risk management, and the creation of novel financial products and services. As AI technology continues to evolve, its integration into financial services will undoubtedly reshape the industry’s future landscape, making it more agile, customer-centric, and innovative.

The journey of integrating AI into financial services is filled with opportunities for innovation, efficiency gains, and enhanced customer engagement. As we continue to explore and harness the potential of AI technologies like Gemini, ChatGPT, and others, the future of finance looks increasingly bright and boundless.

Stay tuned and explore further to harness the full potential of AI in transforming the financial industry for a smarter, more efficient, and customer-centric future.

For a deeper exploration into Gemini’s capabilities and how it compares to other AI models, you can visit the official Google DeepMind and Google AI blog posts​​​​​​​​. https://deepmind.google/technologies/gemini/
https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/
https://blog.google/technology/ai/google-gemini-ai/
https://blog.google/technology/ai/gemini-collection/

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Harnessing Generative AI in Customer Service: The Next Frontier

The integration of generative AI into customer service is revolutionizing how companies interact with their customers, promising enhanced productivity and more personalized experiences. As we delve into the advancements and applications of generative AI tools like ChatGPT, and explore the reliability of AI-driven customer service representatives, we uncover a landscape ripe with innovation yet navigated with caution.

Generative AI: Transforming Customer Service

Generative AI has significantly boosted customer service productivity, urging companies to strategically deploy this technology for maximum value. Initially, businesses are encouraged to adopt off-the-shelf systems for high-value use cases such as enhancing chat channel accuracy before progressing to scenarios that deliver novel products and services to customers, enriching their journey. However, this technology isn’t without its pitfalls. Instances of inaccuracies and potential exposure of sensitive information underscore the necessity for human oversight in more complex applications.

The pace at which generative AI is being adopted within customer service is striking. CEOs now prioritize customer service in their generative AI implementation strategies, acknowledging the technology’s immense potential to redefine this critical business area. The demand from stakeholders, particularly customers expecting tailored interactions, propels this shift. Generative AI is anticipated to directly interact with customers, setting new benchmarks for modern contact centers and significantly impacting customer service operations.

The State of AI in Customer Service

A survey revealed that a vast majority of customer service professionals view AI and automation tools as integral to their strategy. These tools are heralded for their ability to provide 24/7 support and deepen customer relationships. Among the popular AI tools, chatbots stand out for their effectiveness in responding to service requests, followed by generative AI tools aiding in crafting responses. Despite their benefits, concerns about the potential impersonal nature of AI-driven interactions and accuracy of the information provided remain.

Intercom’s Customer Service Trends Report for 2024 highlights AI’s profound impact on customer expectations, which are now higher than ever. With a significant jump in AI adoption in 2023, nearly half of the customer support teams are using AI, a trend expected to surge as 70% of C-level executives plan to invest in AI for customer service in 2024. The report emphasizes AI’s role in transforming the nature of support work and creating new career opportunities, although it also points to a need for aligning leadership and team perceptions regarding role evolution.

Navigating the AI Revolution

As we venture further into the AI-driven future of customer service, companies face the dual task of harnessing AI’s potential while navigating its challenges. The integration of AI demands a balanced approach, leveraging its capabilities to enhance service delivery and customer satisfaction without compromising the human touch that remains crucial in customer service. The journey ahead is marked by opportunities to redefine customer service landscapes, promising greater efficiency, personalization, and innovation.

The evolution of customer service through generative AI offers a glimpse into a future where technology and human ingenuity converge to elevate customer experiences. As companies continue to explore and integrate these technologies, the focus remains on delivering value that resonates with customers, fostering trust, and building lasting relationships in an increasingly digital world.

For more insights on generative AI in customer service:

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A Step-by-Step Guide to Implementing Generative AI in Project Management for 2024

In the rapidly evolving landscape of project management, generative AI stands out as a transformative force. As we step into 2024, integrating this advanced technology into project management practices promises to revolutionize how projects are planned, executed, and delivered. Here’s a comprehensive, step-by-step guide to help project managers navigate the integration of generative AI into their workflows.

Step 1: Understand Generative AI’s Potential

Educate Yourself and Your Team: Before diving into implementation, it’s crucial for project managers and their teams to understand what generative AI is, its capabilities, and its potential impact on project management. Generative AI can automate routine tasks, generate reports, and provide insights based on data analysis, which can significantly enhance decision-making processes.

Resources:

Step 2: Identify Use Cases

Evaluate Your Project Needs: Identify specific areas within your projects where generative AI can be most beneficial. This might include task automation, risk assessment, stakeholder communication, or resource allocation. Focus on use cases that offer the highest ROI and align with your project goals.

Workshop:

Step 3: Choose the Right Tools

Select Generative AI Tools: With a plethora of generative AI tools available, choose the ones that best fit your identified use cases. Consider tools like ChatGPT for communication, Jasper AI for content creation, or custom AI models developed specifically for project management tasks.

Tools Comparison:

Step 4: Skill Upgradation and Training

Develop AI Literacy: Ensure your team is equipped with the necessary skills to work alongside generative AI. This may involve training on AI fundamentals, ethical considerations, and how to interact with AI tools effectively.

Training Programs:

Step 5: Data Preparation

Organize Project Data: Generative AI requires access to relevant, high-quality data to function effectively. Organize your project data, ensuring it’s clean, structured, and accessible for AI integration.

Guide:

Step 6: Pilot Testing

Start Small: Implement generative AI in a small, controlled project environment. This pilot phase allows you to assess the AI’s performance, gather feedback from the team, and make necessary adjustments before wider deployment.

Pilot Program Template:

Step 7: Ethical Considerations and Compliance

Address AI Ethics: Understand and address the ethical implications of using generative AI, including bias, privacy, and data security. Ensure compliance with relevant regulations and guidelines.

Ethics Framework:

Step 8: Integration and Scaling

Seamless Integration: Once the pilot testing is successful, integrate generative AI tools into your project management workflows. Gradually scale the implementation to encompass more projects and teams.

Integration Guide:

Step 9: Continuous Monitoring and Optimization

Monitor Performance: Continuously monitor the performance of generative AI within your projects. Collect feedback, assess outcomes, and optimize the AI tools and workflows for better efficiency and effectiveness.

Monitoring Tools:

Step 10: Foster an AI-positive Culture

Encourage Adoption: Cultivate an AI-positive culture within your team and organization. Encourage experimentation, share successes, and address any concerns or resistance to change.

Culture Building:

Conclusion

Implementing generative AI in project management is not just about leveraging new technology; it’s about reimagining project workflows, enhancing decision-making, and driving efficiencies. By following this step-by-step guide, project managers can strategically integrate generative AI into their practices, setting the stage for innovation and success in 2024 and beyond.

For project managers looking to stay ahead of the curve, embracing generative AI offers a pathway to transformative project outcomes and a competitive edge in the dynamic world of project management.

This guide provides a foundational roadmap for project managers aiming to harness the power of generative AI. As the field of AI continues to evolve, staying informed and adaptable will be key to maximizing its benefits in project management.

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How ChatGPT Helped Me Visualize Global Media Funding: A Unique Application of AI on ApplyingAI.com, Featuring a Dash of Twitter Drama

Introduction

At ApplyingAI.com, we’re passionate about exploring the immense potential of artificial intelligence (AI) in global macro investing, electric vehicles (EVs), autonomy, space travel, and free markets. Our goal is to empower the future of finance and innovation by showcasing real-world AI applications that are revolutionizing industries. In this article, we’ll share a fascinating story of how OpenAI’s ChatGPT helped create an engaging visualization of global media funding, peppered with a few laughs inspired by a recent Twitter thread.

The Idea

In a world where media plays a crucial role in shaping public opinion and influencing decision-making, understanding the relationship between media companies and their funding sources is vital. We wanted to create an impactful visual representation of government funding for major media companies worldwide, including prominent names such as BBC, NHK, CCTV, and CBC, as well as US-based networks like PBS, NPR, and CNN. Little did we know that our creation would coincide with a humorous Twitter exchange!

The Twitter Drama

As fate would have it, a recent Twitter thread surfaced discussing the Canadian Broadcasting Corporation (CBC) and its government funding. The public broadcaster took issue with being labelled as “government-funded media,” arguing that it undermined their credibility. Elon Musk chimed in, suggesting a 70% government-funded label, followed by a tongue-in-cheek compromise of 69% to “give them the benefit of the doubt.” The exchange lightened the mood and highlighted the importance of accuracy (and humor) in media funding discussions.

The Solution

With the assistance of ChatGPT, we generated a Python script that utilized popular visualization libraries such as matplotlib and seaborn. The script produced a striking horizontal bar chart that showcased the percentage of government funding received by each media company, along with the country they are based in. The visualization, complete with vibrant colors and annotations, allowed for an easy comparison of the government funding landscape across the globe – and a subtle nod to the ongoing Twitter debate.



import matplotlib.pyplot as plt

import seaborn as sns



# Data

media_companies = [

    "BBC", "NHK", "CCTV", "France Télévisions", "ARD",

    "RAI", "RTVE", "ABC", "SABC", "CBC/Radio-Canada",

    "PBS", "NPR", "FOX", "CNBC", "CNN"

]

government_funding = [

    75, 95, 100, 80, 85, 70, 90, 95, 20, 65,

    15, 10, 0, 0, 0

]

countries = [

    "United Kingdom", "Japan", "China", "France", "Germany",

    "Italy", "Spain", "Australia", "South Africa", "Canada",

    "United States", "United States", "United States", "United States", "United States"

]



# Set seaborn style

sns.set(style="whitegrid")



# Create a horizontal bar plot

plt.figure(figsize=(12, 8))

ax = sns.barplot(x=government_funding, y=media_companies, palette="viridis")



# Add title and labels

plt.title("Major Media Companies and Their Government Funding Percentages")

plt.xlabel("Government Funding (%)")

plt.ylabel("Media Companies")



# Annotate the bars with the percentage values and country names

for i, (value, country) in enumerate(zip(government_funding, countries)):

    ax.text(value + 1, i, f"{value}% ({country})", va="center")



# Show the plot

plt.show()

The Impact

The resulting bar chart, generated with the help of ChatGPT, has garnered attention and sparked discussions among our audience, as well as some chuckles inspired by the Twitter thread. By understanding the connection between media companies and their funding sources, investors, policymakers, and the general public can make more informed decisions about the future of media, finance, and innovation – and perhaps share a laugh or two along the way.

Conclusion

This unique application of AI demonstrates the power of tools like ChatGPT in simplifying complex tasks and generating insightful outputs, with a sprinkle of humour to keep things light-hearted. We at ApplyingAI.com aim to continue empowering the future of finance and innovation in various domains, such as global macro investing, EVs, autonomy, space travel, and free markets. Stay tuned for more exciting stories, insights, and a few laughs as we continue to explore the captivating world of AI applications!

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AI Job Automation: What to Expect in the Next 5 Years and How to Stay Ahead of the Game

As the world of technology evolves, Artificial Intelligence (AI) is becoming an increasingly influential force in our lives and careers. With the advent of new and innovative AI technologies, we can expect to see a major transformation of the job market over the next few years.

One of the most significant changes we can expect to see is the automation of a wide range of tasks that were once performed by humans. This shift may seem daunting at first, but it’s important to keep in mind that it will also lead to the creation of new job opportunities.

Here are just a few of the areas where AI is expected to have a major impact:

Data entry and processing: With the help of advanced AI algorithms, vast amounts of data can now be processed and categorized with incredible efficiency and accuracy. This will result in a significant reduction of manual data entry tasks.

Customer service: AI-powered chatbots and virtual assistants are already helping many companies handle customer inquiries and support. In the years to come, these systems are only going to become even more advanced and capable of handling even more complex tasks.

Manufacturing and logistics: AI is revolutionizing production processes and reducing the need for manual labor in manufacturing and logistics. This technology can be used to optimize production runs, streamline supply chains, and minimize waste, ultimately improving efficiency and cost-effectiveness.

Sales and marketing: By analyzing customer data, AI can predict which customers are most likely to make a purchase, allowing companies to tailor their sales and marketing efforts with greater precision. AI can also automate tasks such as lead generation and email campaigns, freeing up valuable time and resources for sales and marketing teams.

Healthcare: AI is having a major impact on healthcare by automating many tasks and improving patient outcomes. For example, AI algorithms can be used to process medical images, diagnose illnesses, and develop personalized treatment plans.

As AI continues to shape the job market, it’s important for individuals to embrace new opportunities and develop new skills. Fields such as AI development, data analysis, and cybersecurity will likely be in high demand, and those who invest in these areas will be well positioned for success in the years to come.

In conclusion, AI is not something to be feared, but rather an exciting opportunity to be embraced and utilized to its full potential. The next five years are going to be an incredible time for AI, and we can’t wait to see the impact it will have on the job market and beyond!

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When Stocks Stop Being Sexy – Why I’m Buying Tesla

These are scary times. The market is the most volatile that it’s been since the crash in march 2020 during the first lockdown. Tesla is down over 25% from its high share price to below $660 as of this video. Friends keep asking me: Is it time to sell? Is this the end of the run?

Well, something big is happening and I want to share it with you. 

Tesla is one of the most talked-about companies in the market and one of the most popular to trade. There are hundreds of YouTube channels dedicated to their cars and many dedicated to their stock.

So why should you listen to me?

Please don’t take it lightly when I say that I’m VERY familiar with Tesla. I’ve been following the company since they released the first roadster in 2008. I actually founded a company, InOrbis Intercity, that uses exclusively Tesla vehicles for city-to-city travel, in 2015. During that time I have owned several models of Tesla and have driven and ridden in virtually every model and trim that has been released to-date. We’ve had cars with upwards of 400,000km of use and we’ve driven nearly 3 million km in total with travelers in the past 5 years. We gather feedback from our drivers and our customers on the safety, comfort, maintenance, energy costs, and reliability of Teslas every single day.

I know a lot about these cars and about the company. Let me tell you, until about 5 days ago, I thought Tesla’s share price has been overvalued. And I’ve thought that since 2018.

This is not easy for me. But I’m here to tell you that I was wrong and that I’ve changed my mind about Tesla and also what I’m going to do about it.

Here’s how I changed my mind. And trust me, it wasn’t easy. 

Full disclosure, I’ve been bullish on Tesla’s products for a long time. In my opinion, Tesla makes the best cars on the market. Full Stop. And they only get better every day. We’ve had nothing but positive feedback and experiences in our fleet with the vehicles. There certainly are downsides to owning a Tesla but the software and driving experience make up for any of the negative experiences with the company that we’ve had to this point.

Unfortunately for me (and my wallet), I’ve been bearish on their stock price until now, thinking that it was just the ‘popular kid on the block’ and eventually, the price would come back down to earth. I was sure that Tesla would eventually get bought up by Apple or another large auto-manufacturer and their cars would live on as a sub-brand. I even created an extremely detailed valuation model and wrote a 30-page report on why Tesla was overvalued back in January of 2018 when their stock price was $200 (pre-stock split).

I was absolutely positive that Tesla was not going to make it. They’d soon run out of money and that the only way for them to keep going was if they got bought out.

In my defence, I was almost right! Tesla almost went bankrupt. Apple ALMOST bought them. Elon almost had to give up leading his dream of electrification (twice).

But then they delivered; first on the Model 3, and then the Model Y. They’ve hit target after target and even delivered very nearly half a million cars in 2020, during one of the most difficult years in recent memory for many of us. Tesla has been on an absolute tear for so long that I finally bought in around the time that their stock split. I didn’t buy much though. I still thought they were overvalued and that the run would end.

To summarize, I’ve thought Tesla stock was overvalued for a LONG time.

Lots of people are saying that Tesla would have to have a 50% market share of the entire automotive industry to hit its current valuation. I believed them. Until now.

It turns out that’s just not true!

I won’t go into detail here but in future posts and videos on my YouTube channel, I’m going to show you how, even with an extremely conservative (high) discount rate, Tesla is actually undervalued. And it’s probably undervalued at $800 per share, too. You can check out my valuation by clicking this link.

I’ve changed my mind on Tesla. I’m now bullish on the product AND on the stock.

I am going to buy shares of Tesla, and keep buying until they hit my price target, and maybe even more after that depending on a few factors. I bought shares in after-hours today at a price of $651. If they keep dropping, I’ll keep buying.

As meet Kevin says, I’m throwing my money into the fireplace! As the price of Tesla falls I’ll be Buyin’… The… Dip…!!!

Numbers don’t lie, and I am confident in my numbers.

There’s also a move that Tesla could make that would double my price target. Sign up to my Patreon to find out what that is.

My targets are not based on any dreams of a full-autonomous revolution and of Tesla taking the MaaS (Mobility-as- a-Service) market over with their Tesla Network app (although that certainly wouldn’t hurt my valuation).

My targets are based solely on EV sales and on Tesla’s planned expansion of production. Not on a guess, but on their actual, stated manufacturing targets.

Before I tell you why I’m doing this. Please don’t JUST listen to what I’m saying and start buying because I said so!!! Do your homework! Make your own decisions! I am not a financial advisor so please don’t sue me if I’m wrong!

If you decide you want to buy too. Click this link to get Wealthsimple and get $10 to start trading on top of being able to make trades absolutely free!

OK, here’s what you CAN do and what I did: Make a valuation spreadsheet and understand what the intrinsic value of Tesla is. If you want to learn how to do this, I have a course that I’m building on how to value a company, get more info on that in my Patreon group. 

To get a good head start today, though, just Google discounted cash flow statements and fundamental valuation.

Learn about the business you want to value. Learn what they do and how they do it. Learn about its competitors and the technology that they use. Learn everything you can because you need to know what you’re investing in if you want to be successful. Then, build your model. Predict how much they’re going to make over the next several years and decide if the company is worth investing in. Invest until the business hits your target valuation or until you get new information that changes your mind.

So why is Tesla undervalued?

For me, this all comes down to something that many people glossed over at the time it was announced back in September. The media barely talked about it, because, I think it was too abstract for most people. What is was is Tesla’s internal battery production goal. That’s right. The key factor is how many batteries Tesla is going to manufacture in-house. That number is 3-Terawatt-hours by 2030. That’s huge! It’s 3000x more than what they produced in 2020. And that’s purely for cars and energy storage.

Because that’s their internal production target, and they’ve stated that they’re going to buy every battery their existing partners can make for the foreseeable future. I think it’s fairly conservative to use that 3TWh/year production target as a benchmark for calculating Tesla’s share price. All I had to do from there is work backward to find the size of each car battery and divide to find the number of cars they plan to produce. If Tesla can keep selling as many cars as they can produce (and I think they can because the demand for autonomous EVs is enormous), then this tells me exactly what Tesla’s sales curve is going to look like over the next years. Peek over a few Elon tweets and stats on their expenses and margin targets and we’ve got our future cash flow statement.

Fundamentally, Tesla is leading the way in EVs and in autonomous tech. Those two technologies ARE the future of transportation. They have the technology, they have the manufacturing capacity and they have the talent and the plan to make it happen.

I now think that this will happen and that it’s a great bet. Whether you do is up to you.

Remember: Do your research. If you’re confident in what you’ve found. Take a deep breath and make your call. You can do this.

For now, that’s all.