BlackBerry Will Out-do Bitcoin
The next inevitable and under-priced evolution in the auto-industry is connectivity. It is also the case for industry in general. $BB will be at the core of it.
Note: I´ve moved to SubStack. I´ll still be posting things on Medium occasionally , but now you can find most of my new work here.
Part 1: $BB is coming out of no where, in the eyes of the market. Multiples are catching up.
Part 2: $BB´s QNX is / will be the core of the auto industry 2.0.
Part 3: $BB´s technologies very un-obviously form the basis for Industry 4.0.
FYI: I am long $BB since 20/01/2021. IMO, $BB will 100x by 2025.
The Blackberry I will be discussing likely has nothing to do with the one you once knew. BlackBerry is an entirely new company, focused on software. Within software, it specializes in cyber-security. The company has been totally transformed, as follows:
Before we get started, allow me to lay out a very simple mental model. In my relatively short career as an investor, I have found that there are two core dimensions to thoroughly analyze when buying a stock:
- D1: What the stock actually is.
- D2: What the market thinks it is.
This may be obvious, but in certain occasions, keeping this simple model in mind allows one to see through very persistent illusions that Mr. Market holds in front of our eyes.
Part 1: $BB is coming out of no where, in the eyes of the market. Multiples are catching up.
As you may know, cyber-security stocks are part of the potentially over-priced tech stocks that are out there. Very simply put, $BB is a cybersecurity stock and until very recently, this was not very well known. In fact, I am betting that you are only just discovering this. To summarize:
The price to sales valuations of $CRWD, $PLTR and $SNOW may be somewhat inflated ($CRWD is perhaps most analogous to $BB). However, here we are looking at D2, or rather, what the market think about them. The market thinks they should be valued at around 50 times sales.
At this point, the market is beginning to factor in that BlackBerry acquired Cylance (a company that uses AI to predict cyber-attacks) in February 2019 and that in fact, the acquisition has been successful. It has been successful because $BB cashed in 151m USD through the Cylance unit alone in fiscal year 2020 (first year of operations — most acquisitions do not work), whilst the total acquisition price was 1.4bn USD.
Now, what signals can we pick up about the quality of $BB´s cybersecurity, quality-wise? For the above table to not represent some sort of a distortion in perception, the quality must be quite awful. Shockingly, $BB´s clients include most G20 governments and the top 10 banks in the world. In fact, I was having dinner yesterday with a bunch of people and of course started ranting in autopilot about $BB. I was quite glad when a JPM employee pulled out her smartphone and showed me the BB Workspaces interface.
Arguably, governments are not composed by the smartest people, but banks arguably are (how they use their talent is a different matter). Therefore, are $BB´s offerings so awful as to merit a price to sales multiple of a tenth vs that of its peer companies? There is quite a chance that the delta is not justified and in fact, it seems that $BB is quite a competitive cyber-security company (D1) or at least, a trustworthy one.
Again, the multiples of the other companies may be out of whack, but as D2 evolves closer to D1, the price to sales multiple is most likely to 10X and $BB is likely to join the ranks of the other dubiously valued companies.
Part 2: BB´s QNX is / will be the core of the auto industry 2.0.
The next question is, what potential does $BB have into the future, on top of its increasingly validated cybersecurity business? Cutting straight to the chase, $BB owns a proprietary operating system known as QNX. QNX is an RT-OS (real time operating system) which is designed to operate in critical missions i.e. scenarios where the operating system cannot afford to freeze or malfunction at any point, such as cars and spaceships.
As a quick fun fact, the international space station runs on QNX. Another one is that QNX is installed in 175m cars today, which roughly represents 12% of cars on the road today. Most mid-to-high end OEMs use QNX. $BB has design wins with 19/25 EV OEMs: 61% share in EV market.
You may not know this, but I have spent a large part of the last 3 years in a room learning about and working with neural networks. My peak was last summer when I implemented a 2D autonomous car that could sportively drive (like his father) on a model highway with other cars, without crashing.
As a result, I have gained a very deep understanding on how data and AI are going to change the world / drive GDP. Cars are data producing machines. Every single function in a car yields data and in turn, this data can be used to train AI algorithms that in turn may return predictions. These predictions can / will be of the following type:
- Part X of your car is likely to break in 3 weeks, because you are an awful driver.
- If you take it into the shop now, you will save 4000$, by tackling the problem early.
Other prediction verticals will involve safety, entertainment and route optimization / traffic avoidance. In general, because a car produces vasts amounts of data, processing that data adequately can unlock vast amounts of value. Unlocking this value requires turning cars into smartphones with wheels. This is happening gradually as the many ECUs in cars are gradually aggregated into domain controllers. Enter the connected car.
The evolution of power trains and / or autonomous functions is largely unpredictable. However, the next inevitable and under-priced evolution in the auto industry is connection. Regardless of power trains or autonomous functions, cars are going to transform into software defined and data driven devices, as is evidenced by the current leader in the space, Tesla.
Tesla today is driving a similar dynamic to the one Apple drove when it launched the iPhone. It took the market by surprise and changed the way people saw phones. The rest of the OEMs then had to catch up and they did so by adopting Android as their OS. For laggy auto OEMs today, catching up presents a challenge.
QNX has gained the trust of a lot of these OEMs. This is the key asset that Amazon (AWS) has on-boarded through the BlackBerry IVY co-dev, co-marketing, exclusive 50–50 joint venture, in which $BB keeps the commercial relationship with OEMs and in turn, OEMs and drivers own the data. According to the CEO, a dev version of IVY will be ready in a year and the first auto models with IVY will come in 2023. IVY can / will be retro-installed into the 175m cars on the road with QNX today.
IVY is an end-to-end platform that transforms the data generated by cars into insights / predictions that add value to drivers and OEMs. Very importantly, IVY will be OS and cloud agnostic. The trust that $BB has cultivated through the years is the major enabler of this platform. If it were not, then the IVY deal would not have been so favorable for $BB, the counterpart being Amazon. No trust, no OEMs.
Furthermore, $BB is manifestly not out there to own data, as Google and co have developed a reputation to be, but rather to create an ecosystem where 3rd party developers can create apps that enable OEMs and drivers to extract value. Fundamentally, laggy OEMs could go it alone, but achieving the necessary scale would be an almost impossible feat. Also, OEMs could go for their own Linux be-spoke alternative to QNX, but it would not be security certified and that is very large headache.
In 5 years, QNX / IVY will be to auto what Android is to phones today.
Part 3: BB´s technologies very un-obviously form the basis for Industry 4.0 (including renewables).
The outlined auto trend is by no means limited to the auto space. In general, connecting stuff has a great deal of advantages. Fundamentally, connectivity yields data and adequately processing data yields higher revenues and/or lower costs.
Apart from going down tech rabbit holes, I really enjoy reading history because it rhymes. To gain some perspective on the whole energetic and transportation matter, I read “Energy — A human history” by Richard Rhodes, a review of the intermingling of energetic and transportation advances from the 1600s onward. It turns out that once petrol was discovered by Edwin Drake in the 1800s, it did not really take off until moving petrol around was made possible by advances in iron and in welding (together with advances in organic chemistry in the same century, of course).
Welding enabled the construction of longer pipelines, which enabled the distribution of petrol and hence, petrol began to devour the energetic market . So what? The same is happening with data.
For the last 3 years, I have been delighted with the opportunity to train neural networks for all sorts of things, experimentally. But actually driving GDP with deep learning (neural networks / AI) involves producing high quality data in vast amounts and moving it around safely and effectively — very similar to petrol, as you will discover.
Connectivity will allow the generation of data in industry. At the core of this connectivity will be operating systems of a mission critical nature. Furthermore, transporting this data will involve endpoint management, because it greatly increases the surface of attack for a company. BlackBerry is quite good at endpoint management, as evidenced by its client list (G20 govs + top 10 banks). Incidentally, BB UEM received the NSA certification recently.
If you are still awake;
RT-OS + UEM (unified endpoint management)
=
The Basis for Industry 4.0
Think wind turbines, solar panels, satellites, railways, spaceships etc all connected, generating data (through their RT-OS), that then gets transported to servers (via UEM) where AI algorithms process it and return insights that drive top and bottom lines for operators. What sense does it make to run a factory and not be able to run / monitor the machines from your smartphone? It´s medieval stuff. To learn more about this, read this post I wrote explaining how deep learning will create vast wealth, enabled by the data value chain.
In this trend, incorporate remote work. A remote worker is basically an endpoint. It needs to be connected and secured.
