The last-minute evidence quickly mounted. A letter and an email full of damning claims. Apps that sent self-destructing messages. A payment of $4.5 million to an employee who threatened to be a whistle-blower — and an additional $3 million to his lawyer.
.. On Wednesday, Judge Alsup continued to upbraid Uber’s lawyers for not being more forthcoming with evidence. “I have never seen a case where there were so many bad things done like Uber has done in this case,” he said.
.. The letter that caused the trial to be delayed was written by a lawyer for Richard Jacobs, a former employee in Uber’s security team, to Angela Padilla, the company’s deputy general counsel. Thirty-seven pages long, it detailed a list of questionable behavior at Uber, including spying on competitors and using special laptop computers and self-destructing messaging apps that would hide communications.
.. On Wednesday, Ms. Padilla testified that the letter, which has not been made public in its entirety, was “clearly extortionist” and filled with “fantastical” information.
.. Mr. Jacobs responded, Ms. Padilla said, by sending an email to Travis Kalanick, the company’s chief executive at the time, and others complaining of criminal and unethical behavior inside Uber.
That email to Mr. Kalanick also didn’t surface in the long evidence discovery process between Waymo and Uber lawyers, and was presented in court for the first time on Wednesday.
.. Judge Alsup said to Ms. Padilla that “on the surface, it looks like you covered this up” and tried to keep the letter out of the hands of Waymo’s lawyers.
.. The company did share the letter from Mr. Jacobs’s lawyer with three different United States attorney offices, because Mr. Jacobs had threatened to take his claims to federal prosecutors and Uber wanted to “take the air out of his extortionist balloon,” Ms. Padilla testified.
.. Judge Alsup questioned why Uber would pay so much to an employee making bogus claims. “To someone like me, an ordinary mortal, and to ordinary mortals out there in the audience — people don’t pay that kind of money for B.S.,” the judge said... A Waymo spokesman, Johnny Luu, said in a statement: “Today’s revelations fit Uber’s pattern of destroying and withholding reams of evidence relevant to our trade secrets case, and that those at the very top of Uber were aware of these inexcusable practices. We look forward to the additional discovery granted by the court and to presenting our case in front of a jury at trial.”
Benedict Evans
Winner-takes all effects in autonomous cars
Rather, the place to look is not within the cars directly but still further up the stack – in the autonomous software that enables a car to move down a road without hitting anything, in the city-wide optimisation and routing that mean we might automate all cars as a system, not just each individual car, and in the on-demand fleets of ‘robo-taxis’ that will ride on all of this. The network effects in on-demand are self-evident, but will will get much more complex with autonomy (which will cut the cost of an on-demand ride by three quarters or more). On-demand robo-taxi fleets will dynamically pre-position their cars, and both these and quite possibly all other cars will co-ordinate their routes in real time for maximum efficiency, perhaps across fleets, to avoid, for example, all cars picking the same route at the same time. This in turn could be combined not just with surge pricing but with all sorts of differential road pricing – you might pay more to get to your destination faster in busy times, or pick an arrival time by price.
.. From a technological point of view, these three layers (driving, routing & optimisation, and on-demand) are largely independent – you could install the Lyft app in a GM autonomous car and let the pre-installed Waymo autonomy module drive people around, hypothetically. Clearly, some people hope there will be leverage across layers, or perhaps bundling – Tesla says that it plans to forbid people from using its autonomous cars with any on-demand service other than its own. This doesn’t work the other way – Uber won’t insist you use only its own autonomous systems. But though Microsoft cross-leveraged Office and Windows, both of these won in their own markets with their own network effects: a small OEM insisting you use its small robo-taxi service would be like Apple insisting you buy AppleWorks instead of Microsoft Office in 1995.
.. If you have sold 500,000 AVs and someone else has only sold 10,000, your maps will be updated more often and be more accurate, and so your cars will have less chance of encountering something totally new and unexpected and getting confused. The more cars you sell the better all of your cars are – the definition of a network effect.
.. It could be argued that Tesla has a lead in both maps and driving data: since late 2016, those of its new vehicles whose buyers bought the ‘Autopilot’ add-on have eight cameras giving a near-360 degree field of view, supplemented by a forward-facing radar
.. So, the network effects – the winner-takes-all effects – are in data: in driving data and in maps. This prompts two questions: who gets that data, and how much do you need?
How Self-Driving Cars Could End Uber
Mega-startup’s greatest threat: the disappearance of car-owning drivers caused by the rise of autonomous vehicles
All of this is part of what auto makers and ride-hailing companies anticipate will be a larger transition to “transportation-as-a-service”—potentially the end of widespread vehicle ownership in developed countries. Subscribing to such a service for all of a person’s transportation needs within a typical American city could cost anywhere from 10% to 25% what an average consumer now spends on owning, maintaining and insuring a vehicle, says Tony Seba, co-founder of technology think tank RethinkX. Cost savings on that order could lead to rapid adoption akin to the touchscreen smartphone revolution, he argues... Auto manufacturers have a great deal of experience working with the complex web of dealers, financing companies and fleet managers—even car-rental agencies—that could potentially be repurposed to manage millions of self-driving vehicles.Owing to all these factors, Uber, Lyft and their imitators will eventually cease to exist as stand-alone companies, either going out of business or being acquired by car makers
Cars and second order consequences
Over a million people are killed in car accidents every year around the world
.. it was easy to predict mass car ownership but hard to predict Wal-mart, and the broader consequences of the move to electric and autonomy will come in some very widely-spread industries, in complex interlocked ways.
.. Roughly half of US spending on car maintenance goes on things that are directly attributable to the internal combustion engine, and much of that spending will just go away. In the longer term, this change might affect the lifespan of a vehicle:
.. Car crashes kill 35k people a year in the USA, but tobacco kills 500k.
.. Most estimates suggest that charging a fully electric fleet would lead to 10-20% more electricity demand. However, a lot depends on when they’re charged: if they’re charged off-peak this might not need more total generating capacity
.. In the USA in 2015, there were 13m collisions of which 1.7m caused injuries; 2.4m people were injured and 35k people were killed. Something over 90% of all accidents are now caused by driver error, and a third of fatal accidents in the USA involved alcohol. Looking beyond deaths and injuries themselves, there is also a huge economic effect to these accidents: the US government estimates a cost of $240bn a year across property damage itself, medical and emergency services, legal, lost work and congestion (for comparison, US car sales in 2016 were around $600bn)
.. if you have no collisions then eventually you can remove many of the safety features in today’s vehicles, all of which add cost and weight and constrain the overall design – no more airbags or crumple zones, perhaps.
.. How much more quickly do you get to school in the morning if you drive at the same speed but don’t have to stop at every stop sign just in case there’s someone there?
.. it has been estimated that 14% of the incorporated land of LA county is used for parking.
.. A study in Oakland, in the San Francisco Bay Area, found that parking requirements pushed up construction costs per apartment by 18%.
.. If you remove the cost of the human driver from an on-demand trip, the cost goes down by perhaps three quarters. If you can also remove or reduce the cost of the insurance, once the accident rate has fallen, it goes down even further.
.. it displaces demand from public transport – though the cost of a bus driver is also large part of the cost of the trip, and those drivers might not be needed either, so buses might also be cheaper
.. Does your robotaxi automatically drop you off at a bus stop on the edge of high-traffic areas, unless you pay a congestion charge?
.. one needs to start thinking much more generally, not just about cars, trucks and roads but cities, land use and real-estate. In fact, one needs to think about cities. Cars have remade cities over the past century, and if cars are now going to change entirely, cities will change too.
.. How do cities change if some or all of their parking space, especially in town centres, is now available for new needs, or dumped on the market, or moved to completely different places? Where are you willing to live if ‘access to public transport’ is ‘anywhere’ and there are no traffic jams on your commute? Does an hour-long commute with no traffic and no need to watch the road feel better or worse than a half-hour commute stuck in near-stationary traffic staring at the car in front?
.. What happens to rural pubs if you don’t have to worry about drink-driving anymore?
.. every AV will be watching everything that goes on around it – even the things that are not related to driving. An autonomous car is a moving panopticon. They might not be saving and uploading every part of that data. But they could be.
By implication, in 2030 or so, police investigating a crime won’t just get copies of the CCTV from surrounding properties, but get copies of the sensor data from every car that happened to be passing, and then run facial recognition scans against known offenders. Or, perhaps, just ask if any car in the area thought it saw something suspicious.