Mention Tesla to the average investor and the reaction will predictably elicit one of two responses: praise from the bulls or criticism from the bears.\n\nIn other words, it\u2019s either changing the auto industry forever, or going nowhere fast.\n\nBut let\u2019s set aside the financials, the competition, the shenanigans perpetrated by its ever-entertaining-polymath-celebrity CEO Elon Musk to take a look at the company from a different perspective.\n\nThere is one aspect of Tesla where it is miles ahead of the competition. And that is in its use of data to build what might just be the world\u2019s most sophisticated, cutting-edge neural network anywhere.\n\nWhen big data was the next big thing\n\nSilicon Valley loves buzz words that metaphorically underscore the \u201cnext big thing.\u201d\n\nData, for instance, was \u201cthe new oil.\u201d It was just waiting there, an unrefined asset, ready to be tapped, refined and harnessed to drive competitive advantage.\n\nBut the hype and hoopla over big data has been eclipsed by the nitty gritty reality of the technical challenges in actually converting that structured, unstructured and semi-structured stuff into something truly valuable.\n\nIn fact, we have already sunk into Gartner\u2019s proverbial \u201ctrough of disillusionment\u201d around big data. Hadoop never became the big unified data platform in the sky that seemed so promising 10 years ago.\n\nMeanwhile, artificial intelligence and machine learning are gaining more prominence. But other than the big social media platforms (who are eager to optimize their algorithms to sell you more stuff you didn\u2019t know you needed), who is doing anything of significance here?\n\nTesla\u2019s use of data, AI and ML to build a neural network \u2014 a system of sensors, data, communications, CPUs, peripheral hardware, and software that collectively processes information and adapts and learns like a human \u2014 is where the company really shines.\n\nThe race is on\n\nThe total available market for autonomous transportation is in the trillions, according to analysts.\n\nThat\u2019s why Tesla, Google\u2019s Waymo, Uber and all the big traditional auto manufacturers are scrambling to figure this new model out.\n\nAutonomous driving has, in fact, been infiltrating our human-driving habits for years. Cruise control, ABS braking, lane change guidance, even air bags, can all be considered steps in the path to achieve the Holy Grail, in which humans aren\u2019t involved at all. (Tesla\u2019s Autopilot mode is arguably the most sophisticated driver-assistance system to date.)\n\nGetting to fully autonomous operation, though, is hard work. How can you ensure that a computer on wheels can think, react and make smart decisions when faced with the unpredictable world of insanity that we call driving?\n\nIt will take millions of hours of coding, defining and refining algorithms, sophisticated 3D modeling and simulations, test tracks, even beta testing in real life situations.\n\nAt least, that would be the case if you used the traditional approach. But that is not what Tesla is doing. It is doing something quite novel.\n\nWith 600,000 cars on the road, Tesla treats each vehicle, each sensor, each \u201cevent\u201d (i.e. human interaction with the steering wheel, brake pedals, etc.) as data points.\n\nIt is then taking that data, analyzing it and utilizing it to improve its algorithms, create new algorithms and send those improvements over the air to the vehicles.\n\nAs of November 2018, Tesla has amassed 1 billion miles of Autopilot data. For comparison, Waymo has collected about 15 million miles.\n\nWhen you look at all miles driven on Tesla vehicles (whether Autopilot is engaged or not), the total number of miles logged is around 10 billion, according to Tasha Keeny, an analyst with ARK, an investment firm that specializes in disruptive technologies and markets.\n\nThat is a massive library that Tesla is able to tap into to teach its neural network new things, to adapt and improve.\n\nBut the truly important distinction between Tesla and the other guys is this:\n\n\u201cWhen one vehicle learns something,\u201d said Musk, \u201cthey all learn it.\u201d (Fortune, "How Tesla Autopilot Learns.")\n\nThis has got to be one of the most effective crowd-sourced AI\/ML training initiatives around today.\n\nAutonomy day\n\nHow does Tesla achieve those numbers and drive those continuous improvements into the system?\n\nIn a recent event for investors that Tesla billed as \u201cAutonomy Day,\u201d engineering VP Stuart Bowers* laid out the approach (which can be seen on YouTube; Bowers' remarks come at 2:50\u20133:00 in the presentation).\n\n\u201cTo start all of this we begin by trying to understand the world around us,\u201d said Bowers. \u201cWe have eight cameras, but then we have 12 ultrasonic sensors (radar), an inertial measurement unit, GPS, and the one thing we usually forget about: the pedal and steering actions.\u201d\n\nEach of those sensors, noted Bowers, has \u201coverlapping fields\u201d that audit each other. With this approach, Tesla is able to get \u201can extremely precise understanding of what\u2019s happening.\u201d\n\nEach new event, each new interaction between driver and machine, is logged and uploaded to its database. It\u2019s then used to create 3D simulations, which Tesla software engineers can study to improve and refine the algorithms.\n\nUpdates, changes or modifications to the overall system can then be transmitted over the air to Tesla vehicles.\n\nShadow mode\n\nAnd here\u2019s the brilliant part: Before going \u201clive\u201d with those changes, Tesla operates the modifications in \u201cshadow mode.\u201d This is, without a doubt, a major improvement over simulation, or standard beta testing, since shadow mode is operating in real time, in the real world, but \u201cthinking\u201d in the background, to create a continuous feedback loop.\n\nIn simple terms, think of it as your teenage self, eager to get your license, sitting in the passenger seat as Dad drives and explains what he\u2019s doing. You pay very close attention, because you\u2019re going to be in that driver seat soon.\n\n\u201cWhen we have new algorithms we want to try out, we can put them on the fleet and see what they would have done in the real-world scenarios,\u201d commented Bowers.\n\n\u201cUltimately, we can do more and more through machine learning and then go into a controlled deployment, which for us is the early access program.\u201d\n\nOne feature Tesla is testing right now is a predictive behavior as to what a pedestrian or cyclist in front of a vehicle might do.\n\n\u201cWe have the ability to detect an obstacle in the roadway and a pedestrian is an obstacle,\u201d said Bowers. \u201cWe can actually look at bicycles and people and our next-generation automatic emergency breaking system will not just stop for people in your path, it will also stop for people that are going to be in your path.\u201d\n\nThis new feature is operating in shadow mode right now, noted Bowers. It will then go out to the entire fleet of Tesla vehicles but will be further beta-tested by a hard-core group of drivers who have signed up to participate.\n\nAnother example is lane changing on a freeway. Tesla now has amassed 9 million successful completions using Autopilot without one accident. \u201cWe are doing about 100,000 of these a day,\u201d said Bowers.\n\nAnd as of April 2019, it has logged 70 million miles in which Autopilot combined with its GPS navigation have taken drivers and passengers door to door.\n\nThe ultimate endgame, according to Bowers, is \u201cto look across all the neural networks and all the cars and bring all that information together and ultimately output one source of truth for the world around us.\u201d\n\nMobility as a Service\n\nThe rush to autonomous driving is not to sell you a robot car. It\u2019s to rent you a robot taxi service.\n\nThis is the emerging field of Mobility as a Service. In a recent interview with me, ARK\u2019s Tasha Keeny argued that MaaS is already under way in a sense, if you consider our increased use of Uber and Lyft, along with traditional taxi services.\n\nBut it is still more expensive to \u201crent\u201d than buy, according to her numbers. Today, car ownership runs about 70 cents a mile, cheaper than using a service. But once human labor is out of the equation, the price drops precipitously to 22 cents a mile for MaaS, far cheaper than owning.\n\nAnd rest assured that Milennials \u2014 the sharing generation \u2014 will be first in line for this service.\n\nThis tipping point \u2014 when we no longer own cars but tap an app on the phone to summon a driverless vehicle \u2014 is going to create a market worth $5 trillion (that\u2019s trillion with a T), says Keeny.\n\nThis is the moon shot for all the companies involved in autonomous driving. This is why Uber\u2019s investors are willing to accept $6 billion in losses this year alone. This is why Waymo has partnered with Avis and Autonation.\n\nTesla plans to get into the MaaS game as well. New Tesla Model 3s, for example can be leased, but you can\u2019t buy the car at the end of the lease. Tesla is preparing to use them for a semiautonomous type of MaaS as it transitions to fully autonomous MaaS in the near future.\n\nThe stakes are high. The trophy is big. And a world of mobility as a service is coming at us fast. The end result will be game changing.\n\nAt least from a technology perspective, Tesla is a full lap ahead of the competition.\n\nThe big auto manufacturers are doing what they do best: designing and building vehicles at scale to drive down cost and remain competitive in selling vehicles.\n\nBut as is usually the case with disruptive companies, Tesla doesn\u2019t abide by those rules. Tesla is building its own chips, its own hardware, software (from the kernel up), and most importantly, perhaps, the neural network, all in an effort to offer its flavor of MaaS. The production of physical vehicles is just one piece of the equation.\n\nTesla\u2019s game changer in this holistic systems approach is, without a doubt, data.\n\n*Bowers has since left Tesla.