Article exploring Tesla's unique approach to developing self-driving technology by gathering real-world driving data.
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These are all fair points. I would also add:
* Lost revenue for auto makers from increased car utilization, enabled by autonomous cars combine with on-demand services
* Reduced income for city’s once demand for paid public parking decreases
The way a Silk Road clones could endure is to build a community supported marketplace, similar to how bitcoin crowd sources the verification of transactions.
In Alonzo Harris’ timeless words “to protect the sheep you gotta catch the wolf, and it takes a wolf to catch a wolf,” and the FBI has plenty of wolves. But as Alonzo would say, “this is chess, it ain’t checkers,” and by crowd sourcing the support for the marketplace the FBI would be playing a game of wack-a-mole, taking down one member, and two will fill his place. It needs to be an inherently decentralized organization, just like we learned with Valve.
Regarding the underwater pressure resistant tank, are you suggesting the operator should live there, or they should just store servers there? If it’s the latter, then again I think a decentralized approach could work well, or in conjunction. By purchasing (or building) cloud computing capacity across many nations, you could have a network that is robust to any single node going down, and when one is taken offline the community could respond by rebuilding in other regions.
Diversifying servers also makes sense from a supply chain standpoint. If a provider goes out of business, or has technical difficulties, you are considerably less dependent on them. A recent example of this is how Apple started using Intel modems in some iPhones, in addition to using Qualcomm’s modems in other iPhones. This makes Apple less dependent on a single supplier, and allows them to balance if one supplier is unable to meet their demand.
Do you think that more conversations with the electorate would have predicted the outcome of this past election?
Nate Silver (fivethirtyeight’s Founder and Editor in Chief) was the one who consistently said that Trump had a reasonable chance of winning the election, whereas his competitors were giving Trump single-digit odds. While the polls were off, he uses a probabilistic model for exactly this reason. There were a few things that broke in Trump’s favor on election day, and that proved to be the difference.
The polls ended up being off by 2 percentage points[1], which is within the accepted confidence margins. Even though the outcome surprised a lot of people, this shouldn’t be remembered as a polling error, but rather a reminder that polls are inherently an input to a probabilistic model, not a deterministic one.
Source:
[1] http://fivethirtyeight.com/features/what-a-difference-2-percentage-points-makes/
Only time will tell before fully autonomous cares are generally available. I’ll bet ya $20 they’ll be available within 5-years, interested?
As for the flying car point, I would refer you to this source: https://xkcd.com/1623/
And my focus is on when they initially become available to the general public. One major limiting factor for a broad transition to autonomous vehicles is transitioning over the industrial base. The current global production capacity is 100M vehicles/year, and there are ~1.2B cars and trucks on the road, so even if the number on the road remains constant (which seems conservative given population growth and increasing wealth in countries like China) it would take 12 years (albeit retrofitting solutions could cut into this somewhat).
Sources:
Global production capacity: https://www.statista.com/statistics/266852/capacity-of-the-global-automobile-production-industry/
https://www.statista.com/statistics/281134/number-of-vehicles-in-use-worldwide/
To clarify, Tesla likely wouldn’t use the data to emulate how drivers behave today. Rather, it could identify areas where the algorithm is incorrect today, and fix just those areas.
One example is if the sensors misidentify something as an obstruction in the road. If multiple human drivers keep going and there is no crash, the algorithm would adapt to prevent these types of unnecessary braking events.
As for hiring professional drivers, the problem is cost. Google has essentially taken this approach, and as a result the scale of data collection is severely limited.
It sounds like the players so far have primarily tried to compete symmetrically with brokers, with a strategy (at least initially) of totally replacing the broker.
Strategically, it feels like there are 2 alternatives.
1) Automate some tasks so that brokers become more productive. This should lead to fewer total brokers needed (per some number of houses), and those brokers would make more money. Then long term, I would expect competition to drive down the revenues brokers can capture, decreasing the cost to sellers, and shifting some of that value to software companies.
2) Find markets where the benefits are preferred, while the drawbacks are immaterial. For instance, rentals may be a better market than houses for sale, since it involves less of an investment (per Dave’s point). You could also start by targeting first-time renters who would otherwise advertise on craigslist. In addition to offering a more targeted platform (it also wouldn’t be hard to make it look better), you could also offer services such as standard rental contracts, and even a payments system that automatically deals with delinquent payments (and documents these in case things get bad).
Have you seen anyone attempting to pursue either of these strategies?
One note – published tuition for 4-year colleges has been increasing (i.e. ‘sticker price’), but inflation-adjusted net tuition has been flat for the past 2 decades. This is because the increase in sticker price has been offset by school scholarships, federal and state scholarships, tax benefits, employer aid, and private scholarships.
However, this has also increased the variance in cost across students. Some students are more than offset by this benefit, while others are stuck paying the fully published price.
Check out this graph: http://imgur.com/a/4x8Gw
Source: https://trends.collegeboard.org/sites/default/files/2016-trends-college-pricing-web_0.pdf
Interesting. It’s disheartening to hear other players aren’t following suit. Of all companies, I would expect ones that promote an outdoor lifestyle to be leading the way.
Regarding their Black Friday ad, I know REI does something similar. They actually close their doors and encourage people to get outside – a bold move for a retailer.
@Jesse it depends on what dimension of competition you’re interested in.
Regarding environmental impact, AWS is definitely a laggard – Microsoft and Google both claim to be carbon neutral, and are focusing on data centers in particular since this is a major source of energy consumption.
As for IBM, I’m not familiar with any environmental initiatives, but as a cloud infrastructure player, they are way behind these 3.
Sources:
Google – https://cloud.google.com/about/data-centers/
Microsoft – https://www.microsoft.com/about/csr/environment/
Azure specifically (Microsoft cloud) – https://www.microsoft.com/about/csr/environment/solutions/cloud/
Interesting point, I agree that if elasticity truly is 0, then these subsidies are difficult to justify.
I do think there is another positive externality besides developing a national charging network. The automotive industry has a massive minimum viable scale, and the cumulative experience from manufacturing internal combustion engine vehicles has helped drive costs down while improving performance for decades. This presents a very tough climate for a new technology, which cannot immediately achieve the same scale, and requires reinvestment economics in order to improve the technology and manufacturing processes for an electric vehicle. By starting with a smaller market (luxury sedans), Tesla has been able to ramp up production, refine it’s manufacturing, and improve electric vehicle technology as it prepares to launch the mass-market Model 3.
Interesting, like Alex I also wasn’t aware of these initiatives.
Alphabet has had the privileged of consistently strong growth, high margins, and a very defensible business. To what extent will they come under shareholder pressure to cut costs once the growth of their marketing business starts to stabilize? One of the reasons I’ve heard for structuring the company under Alphabet was because of investor pressure to have more transparency on spending. Now that investors have this transparency, it feels like only a matter of time before they start to question large cost centers that fail to meet Wall Streets desire for short-term profits.
Very interesting!
Given they are still a startup going up against some very large companies, how do you suggest they approach their lobbying efforts? It feels to me like if it becomes a lobbying arms-race they are at a significant disadvantage given their limited capital relative to the companies interested in maintaining the current subsidies.Also, have you ever tried one of their burgers? How was it?