Microsoft, Facebook, trust and privacy

There are strong parallels between organised abuse of Facebook and FB’s attempts to respond, in the last 24 months, and malware on Windows and Office and Microsoft’s attempts to respond, 20 years ago.

Initial responses in both cases have taken two paths: tactical changes to development and API practices to try to make the existing model more secure, and attempts to scan for known bad actors and bad behavior (virus scanners then and human moderators now)

For Microsoft’s malware problem, however, this was not the long-term answer: instead the industry changed what security looked like by moving to SaaS and the cloud and then to fundamentally different operating system models (ChromeOS, iOS) that make the malware threat close to irrelevant.

Facebook’s pivot towards messaging and end-to-end encryption is (partly) an attempt to do the same: changing the model so that the threat is irrelevant. But where the move to SaaS and new operating systems happened largely without Microsoft, Facebook is trying to drive the change itself

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Is Alexa working?

Amazon’s Alexa has been a huge, impressive and unexpected achievement. Amazon created a category from scratch and left both the AI leader Google and the device leader Apple scrambling in its wake. It’s now sold 100m units. So far, though, this success is pretty contingent - we do still have to ask what Amazon actually gains from this. What do consumers do with these devices that helps Amazon? What fundamental strategic benefit does it get? Amazon has put an end-point into tens of millions of homes - what does it do with it?

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Does AI make strong tech companies stronger?

Machine learning is probably the most important fundamental trend in technology today. Since the foundation of machine learning is data - lots and lots of data - it’s quite common to hear that the concern that companies that already have lots of data will get even stronger. There is some truth to this, but in fairly narrow ways, and meanwhile ML is also seeing much diffusion of capability - there may be as much decentralization as centralization. 

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Presentation: The End of the Beginning

Close to three quarters of all the adults on earth now have a smartphone, and most of the rest will get one in the next few years. However, the use of this connectivity is still only just beginning. Ecommerce is still only a small fraction of retail spending, and many other areas that will be transformed by software and the internet in the next decade or two have barely been touched. Global retail is perhaps $25 trillion dollars, after all.

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Tesla, software and disruption

When Nokia people looked at the first iPhone, they saw a not-great phone with some cool features that they were going to build too, being produced at a small fraction of the volumes they were selling. They shrugged. “No 3G, and just look at the camera!”

When many car company people look at a Tesla, they see a not-great car with some cool features that they’re going to build too, being produced at a small fraction of the volumes they’re selling. “Look at the fit and finish, and the panel gaps, and the tent!”

The Nokia people were terribly, terribly wrong. Are the car people wrong?

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Ways to think about machine learning

We're now four or five years into the current explosion of machine learning, and pretty much everyone has heard of it, and every big company is working on projects around ‘AI’. We know this is a Next Big Thing. I don't think, though, that we yet have a settled sense of quite what machine learning means - what it will mean for tech companies or for companies in the broader economy, how to think structurally about what new things it could enable, and what important problems it might actually be able to solve.

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