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Looker is a data analytics and business intelligence startup.  Their software is used for exploring data and making decisions for everything from marketing, sales, operations, logistics, web, etc.  It is able to pull and digest data in real time from numerous sources including Salesforce, Slack, SQL, Hadoop, web APIs and produce a number of dashboard visualizations and analytics. 

Looker's business model is a standard SaaS subscription model.

The team is based in the San Francisco bay area.

Why I like Them

Companies currently are flooded with data in the "Big Data Age" but understanding that data and being able to use it to make better decisions is something many firm's struggle with.  Today, to make any sort of headway with their data, specialist data scientists with years of advanced education and expertise in tools like SQL, R, etc. are needed.  However, every job function in a company needs to be able to quickly analyze and make decisions off this data without going through the bottleneck of a limited number of data scientists.  Looker provides this ability to every person in the company without the bottleneck and without needing advanced training to use and analyze this data.  In our data driven age this is a critical ability and it's easy to see Looker becoming a standard tool utilized across every levels of a company just like Microsoft Office Suite.

Despite how crowded the Business Intelligence space is, Looker has a lot of tractionI at over 1,000 paying customers and more than 50,000 users.  These include some large names such as Square, IBM, WeWork, Nordstrom, and Amazon. 


Disclosure:  All information is from publicly available sources, I have not had any contact with a member of the company or its investors.


Some Thoughts on Voice


Lately, I've been thinking about the rapidly emerging voice technologies, a subset of artificial intelligence and machine learning.  Most of the big technology companies now offer a voice assistant through a mobile phone or smart speaker such as Amazon with Alexa, Apple with Siri, Microsoft with Cortana, etc.  Forecasts predict that 50% of all searches done online will be through voice by 2020 (driven by mobile), with an estimated 13% of US households owning a smart speaker today.

My take is that voice is another interface, not a platform in and of itself or a new paradigm in computing.  Voice is inherently a low bandwidth medium making it strong for certain types of activities, specifically giving commands and getting information to basic questions.  “Alexa play music” or “Google what is the weather going to be today?”.  This is borne out by the plethora of studies being released lately examining how consumers use voice technologies.  The most common uses of voice today by far are asking for directions, asking a quick question, calling someone, checking time, or playing music.  

I expect to see the voice interface dominate when it comes to certain use cases such as home automation but I suspect it’s overhyped for a lot of other common digital use cases.  A big one in this bucket would be online shopping.  Humans are inherently visual creatures and the ability to see a product and read about it will trump being able to purchase purely through a voice dialog.  The only exception to this would be repeat purchases of the same product or if it’s a really basic item that you are agnostic to its brand (“Alexa, order me a stapler”).  Obviously, Amazon disagrees with me having stated it has over 5,000 employees currently working on Alexa and Echo technologies but I remain bearish on voice for eCommerce.

In terms of search, due to its low bandwidth I doubt voice will be very useful for discovery in general or for new customer acquisition by brands and advertisers.  It’s just not a good interface for comparison or discovery.  That of course won’t stop Google and Amazon from offering a new type of ad unit in the very near future where you can pay these companies to be a featured product in a voice search.

Noted technology pundit Scott Galloway for the last year has been predicting the death of brands due to voice but I have to disagree with him here and think he is greatly exaggerating the impact this technology will have.  Voice will simply becoming one of several interfaces users have access to as it seeps into common usage, but it certainly won’t replace touch, keyboards, etc. 




DeepScale AI is a computer vision startup focused on real time automotive perception systems that give cars self driving capabilities.  The team deploys onto cars pre-trained neural networks able to take information from any type of sensor system (including cameras, LiDar, etc.).  They are focused on "efficient" deep learning that is high powered but doesn't rely on expensive and specialized hardware.

DeepScale has a standard software licensing model that charges per sensor and per car to the manufacturer.  Their technology has already been licensed by a number of manufacturers and will be in customer cars in the next few years

The team is around 20 employees and is based in California. 

Why I like Them

What most people don't realize is that existing autonomous vehicles such as Waymo's are massively expensive.  The industry isn't cost-sensitive currently but it will have to become extremely cost conscience as it scales.  The current modus operandi for solving deep learning challenges is to throw more expensive hardware at it.  Automotive manufacturing is a low margin business and would never reach close to profitability with autonomous cars cost structure as they are being developed today.

 I like that DeepScale has thought ahead on this and is focused on creating computer vision systems that work on cars existing electronics (known as ECUs) rather than requiring additional specialized and expensive chips to be installed.  Under the current cost structures of automobile manufacturers, autonomous vehicles will not be able to be sold even close to current car prices, so the team's focus on the business side of the technology is particularly noteworthy.  By using systems already in the latest vehicles, they help automotive manufacturers save on cost by doing more with less.  Think of DeepScale as building efficient automotive AI.  

I also like the competitive nature of the product.  Their systems continue to gather data in the field and are able to be continuously updated with upgraded neural networks pushed to cars remotely that have already been sold.

Disclosure:  I have spoken to members of the team.

Where are the New S Curves?

S Curves are a business concept forecasting out the growth and development of new innovations as they progress through their technology life cycle.  They generally are considered to have 4 phases starting with Research and Development, Ascent where the technology starts seeing some real usages, Maturity where the technology is well established with slow growth remaining, and the Decay phase where other new technologies surpass it.  Wikipedia goes into much more detail here.  

S curves matter because they help you identify when the optimal time to invest is and when it's time to move on.  In other words, they are helpful in predicting growth and getting in front of it.  The big take away from the idea of S curves is that technology does not grow linearly, you have a long period of little to no growth with very few adopters, and then in a short time frame (1-3 years) the technology blossoms with it appearing to most people as coming out of nowhere  by magic to alter their daily lives.  

The dominant S curve of the last decade has easily been mobile, but its been in the maturity phase for the last few years.  Another S curve in the mature phase would be social media.  Every decade or so there is a dominant S curve but there are simultaneously other, smaller S Curves, that likely aren't as impactful in reshaping society but still bring about permanent change.

Some of the new S curves I am watching closely and where I see them in their cycle:

  • Artificial Intelligence - Despite all the hype this one is very early in the R & D phase with a ways to go.  We haven’t seen anything here yet.  This will likely really become the dominant S Curve of its time 10-20 years from now.
  • Drones - Ascent phase.
  • Autonomous Cars - R & D phase but will go into the ascent phase by 2020.  This will likely be the next big one after mobile over the next decade in that it will reshape society.
  • Blockchain - Ascent phase, even though with this one I don't think it will be as big as many people and the mainstream media will have you believe.
  • Genomics/CRISPR - Very early R & D phase.  We haven't even scratched the surface.
  • Virtual Reality - The ascent phase but I don't think virtual reality will be nearly as big or society changing as other S curves on this list.  I remain a sceptic on this one.
  • Augmented Reality - Solidly R & D phase but I think we will start to see it hit the Ascent phase in the not to distant future.
  • Alternative Energy sources/Battery technology - Early ascent phase but moving much more quickly than people think.  Check out my post here for more details. 
  • Cloud Computing - End of the ascent phase/beginning of the maturity phase.
  • Voice Interfaces - Beginning of the ascent phase.
  • 5G Technologies/IoT - About to hit the ascent phase as they come out of R & D.  The interesting thing here is I see 5G as being the catalyst that really kicks IoT (aka sensors being everywhere and in everything) into the ascent phase as well.  These S curves are even more intertwined than others.
  • Computer Vision - A subset of Artificial Intelligence above but one that is moving much faster along the S curve than other technologies that make up AI.  Currently in the beginning of the ascent phase.
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The State of AI 2018

Nathan Benaich and Ian Hogarth just released a great (and long) deck on the current state of AI as of the end of June 2018.  It is packed full of excellent information and data. 

A few items of interest that stood out to me:

  • The rise of Transfer Learning where a model trained on one data set for one task can be applied with much less training to a new and different task.
  • That AI right now seems to be more about having newer and better hardware (GPUs and TPUs) than anything else.  The limiting component for deep learning today is hardware with GPUs best for offline training of AI models, necessiating large CapEx investments in expensive hardware in the coming years for tech companies that want to remain competitive.
  • AI has become a service offered by the big cloud providers.
  • Some great case studies of AI being used today to disrupt different industries including pharma, Enterprise automation, transportation and cybersecurity.
  • One of their last slides has their big AI predictions over the next 12 months - I will be genuinely curious to see how many they get right.