Thoughts on Tesla’s Risks

Had a chat with a mentor and friend about Tesla recently and he asked about the rising risk profile on Tesla and concerns about the stock and market overall. These were my thoughts:

The current risks (that he mentioned):

  1. Departing Board Members
  2. Outside looking view of change in Autonomous Driving (or “Robotaxis”)
  3. Slumping sales
  4. Federal Tax Credit Ending
  5. Increasing Competition
  6. Quality Issues
  7. Support Problems
  8. Logistic Issues
  9. Convertible Bonds raise of $2.7B

Other risks (I mention):

  1. Macro factors: recession looming
  2. Side effects on Automotive market due to Tesla effect
  3. Labor effects on Automotive market and negative sentiment effect
  4. No effect on global temp change due to Tesla push
  5. Resource crunch due to hindrance by negative forces

Headwinds (2019):

  1. China Gigafactory Completion in May
  2. Buffalo Gigafactory Ramp-up in Q2
  3. Reduced cost to operate for Automotive
  4. Increased sales of FSD (baked in to Automotive Sale)
  5. New paid software features
  6. Multiple quarters of positive new cash flow (thus, incorporation of Tesla into S&P 500)


  • Is climate change real? If agreed upon yes, then entire entire energy consumption to usage lifecycle [ECUT Lifecycle] needs to be rethought towards renewables
  • How much is Automotive and Energy Consumption+Usage Utility Market worth? Both are $3T markets (Tesla is tackling both)
  • Is Tesla the primary leader in the new market? I’d say yes based on demand and execution.
  • Will Tesla continue to be the primary leader going forward? I’d say yes because they have the best lead and they’re moving faster. With even better products likely to be built off of great design and data.
  • What does a market leader generally comprise of in market share? Anywhere between 10-50%.

With all of those laid out (I’m sure I missed some), the simple investment question is, “do you believe climate change is real?”. I’ve believed yes since 2009 and wanted to see someone executing on fixing the problem. I’ve learned that it’s not just a problem to fix, but an innovation cycle that will make things better. Tesla is a safe bet, as long as it has cash, to become gigantic…anywhere from a $600B -> $3T market cap even though it’s sitting on ~$36.7B today. If you take a 5-10 year view out and look at the situation, I’d say Tesla is in the 3rd-4th inning of a fight to completely replace our ECUT Lifecycle against the incumbents that have spent the better part of 100+ years owning it and established companies, organizations, and national powers around it. This all likely affects practically everyone on the planet.

You’re, essentially, betting that Elon Musk and Tesla (and everyone that works and/or roots with/for them) is there in order to fix one question: is climate change real? If so, then that means the likelihood of the entire human civilization being wiped out is possible. It’s very …extraordinary and the stakes sound really dire and out of a comic book. Well, it’s been scientifically proven to be accurate even by Exxon/Mobil back in 1982. That’s the situation that we are in. From the risks outlined, the Tesla strategy seems to be to replace the world’s ECUT Lifecycle and to use the current lifecycle where the weather is increasingly working against them (e.g. Hurricane Maria and Puerto Rico).

So, slumping sales, volatility of board members, “quality issues”, and “support issues” can be put under a stress umbrella under an aggressive worldwide strategy and effort to ECUT products around the world from only a ~$36.7B company (e.g. Facebook is worth ~$529.2B today) right now. Also, this is starting to undercut sales of other very large companies in the same huge and important market as we see in US April auto sales (most unsold ever). As a leader, how do you deal with this situation other than keeping on pushing forward in the 3rd-4th inning rather than throwing up your hands and saying, “oh shit, this is too much and too many problems to deal with!”? I think that’s where Robotaxis have come in after Tesla’s announcement of them in their Autonomy Day.

Tesla’s well-communicated out vision since 2015 has incorporated this concept due to huge under-utilization of the concept of the car of 5% of time being used for anything other than a parked state. I believe Tesla’s going to find out just how much that utilization rate can be increased by deploying the Robotaxis (maybe 1st in Norway) and seeing how profit margins can increase. It’s incorporated into the design of the car/vehicle itself with 1M miles as a design constraint. Why not push the bar?

With every % increase in utilization, the less cars need to be sold (out of 97M+ annual vehicles global sales market). We’re in this massive innovation change and we’re (i.e. every human being on the planet) all a part of it at this point to make sure climate change is averted now that most of the world (and hopefully the entire world) understands that it’s scientifically proven for decades, known, and understood. Hope we figure it out in time.

Climate Mood

Continuing with the delve into Climate Change, looked into understanding how people feel, positive/neutral/negative, about the subject on Twitter. Using TextBlob, with Tweepy, Pandas, and Plotly Dash…I built initial revision of a data visualization dashboard, Climate Mood, that analyzes tweets over a sample of the last 2,000 collected hourly. The idea is to provide a simple, sentiment-focused look across multiple analyses. I’ve particularly enjoyed building and re-applying some of the methodologies and statistical analyses I’ve learned recently.

Go to the dashboard

As mentioned, the dashboard pulls from Twitter over an hourly basis and deposits into a PostgreSQL database. The top chart provides a linear regression trend line (unevenly for now) across tweets collected that can be toggled. Also, tweets can be seen on the top-right tweet-box on hover. Other simple sentiment analyses include average verbosity of tweet length, popularity of tweets, and average sentiment-based amplitude of tweets. Feel free to check it out!

Looking forward to applying more statistical analyses across these auto-updated samples. If you have any feedback or suggestions, please submit an issue on the GitHub repo or contact me.

Climate Trends

Been thinking about climate a bunch lately and started looking into trying out the latest data tools and trends out there on the Internets. I’ve particularly enjoyed R and Pandas/Python, especially via Plotly Dash, and re-applying some of the methodologies and statistical analyses I’ve learned while on the job. The latest curiosity is in global temperature change since 1910 and various other societal factors. Lo and behold, the first iteration and not-so-polished, Climate Trends.

You can view the dashboard here

The dashboard has a drop-down menu of various analyses around global temperature change and linear fits across birth rates, fertility rates, death rates, life expectancy at birth, and rural population growth rate as well as a nominal years passed chart. Feel free to dabble!

If you have any feedback or suggestions, please submit an issue on the GitHub repo, respond on Dribbble, or contact me.

My Investment Research Checklist

I was talking to family and friends about various investments made (and some missed out on) over the years. Have gotten decent at picking winners (using a Value-Investment framework inspired by this book that mixes intuition, selection, and analysis). Here’s the base-level rubric for what I look into before I investment into any company. Some analyses go further. Generally, 9 months to 1 year is taken before any action…essentially driving the associations of the entity into muscle memory and relying on intuition, alongside any other company/product/market, to make the call.


  • Read on 3-6 months of articles on stock and target company
  • Read on 3-6 months of analyst coverage on stock
  • Find Market Cap potential of target company
  • Find Market Leader
  • Identify Competitor Set
  • Identify Lead Competitor(s)
  • Find Growth Darkhorse(s)
  • Identify any market regulation


  • Determine CEO capabilities and habits
  • Determine CEO’s style of communications (past 2 years)
  • Identify Product Roadmap
  • Read last 5 product announcements
  • Linkedin Lookup on Corporate/Employee Composition (specifically product/marketing/engineering)


  • Read past 10 10-Q, 2 10-K
  • Identify, if possible, Sales & Marketing + R&D spend
  • Identify, if possible, Growth of S & M + R & D spend

Would love to hear your thoughts! Feel free to DM via LinkedIn.

SJ Charts

SJ Charts is available now fully open-sourced here! has been fun and the documentation, as well as the community forums, have been great to work with. Wish there was more in-grained analytics support via Google Analytics and Mixpanel, but that seems to be a work-in-progress. Here’s what it looks like now:

The charts now have time-based charts for Housing Prices, Unemployment rate, and Jobs by Sector for the City of San Jose. The idea behind this app is to show various analyses via a top-level tabbed navigation for the City of San Jose residents. Basically, to build out useful analyses for the open data available from the City of San Jose.

San Jose City Data Analysis Fun

So, I took a few weeks to get on-ramped with Pandas 0.23.0 and Python and am still learning the ropes. Though, I wanted to share some output. Namely, using the San Jose Open Data Portal, there were some good insights to be had from looking at economic data pushed out by the city for 2006-2016.


What did I do?


  • Looked at unemployment rates across various housing prices (condos/townhomes & single-family homes) using ordinary-least-squares regression.
  • Looked at, over time, labor force and housing price changes.


SJ Economic Changes, 2006-2016



Condo/Townhome prices vs. SJ Metro Unemployment



Single Family Homes prices vs. SJ Unemployment


What did I find? Namely, housing prices are increasing rapidly compared to labor change (i.e. job growth) in San Jose over the course of 2006-2016. Also, there’s a strong correlative relationship between something that we all take for granted and as obvious: increased housing prices in San Jose runs with lowered unemployment rates.

If you’d like to look at the data analysis done, here’s the GitHub repo. If you have any questions/comments/suggestions on more analysis, feel free to contact me via Twitter, GitHub, or LinkedIn.