Netflix is not a tech company

Like Sky before it, Netflix is a television company using tech as a crowbar for market entry. The tech has to be good, but it’s still fundamentally a commodity, and all of the questions that matter are TV questions. The same applies to Tesla, and indeed to many other companies using software to enter other industries, especially D2C - what are the questions that matter?

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Notes on AI Bias

Machine learning is the new centre of tech, and like all big new things there are issues. ‘AI bias’ is much-discussed right now: machine learning finds patterns but sometimes it finds the wrong one, and it can be hard to tell. This is a real concern, but it’s also manageable as long as we pay proper attention to it, and will probably look much like similar issues in previous waves of automation.

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Apple Plus - brand versus subscription

Apple’s talk about services got specific with a bunch of news subscription services. Most of them are sensible and worthy iteration, but the company still hasn’t explained exactly what it plans with its push into commissioning billions of dollars of premium TV (Spielberg! Oprah!). Maybe all of this is about trust: the old Apple promise was that you don't have to worry if the tech works, and the new promise is you don't have to worry if the tech is scamming you. 

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Smart home, machine learning and discovery

Smart home today looks a lot like the world of kitchen gadgets a few generations ago - and so does machine learning. We have a bunch of cheap commodity components (DC motors! Cameras! Wifi chips! Voice recognition!) and we’re trying to work out how to bolt them together into things that makes sense. There are lots of experiments - some things will be the toasters or benders of the future, and some will be the electric can-opener.

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Microsoft, Facebook, trust and privacy

Facebook’s struggle with abusive behavior today looks a lot like Microsoft’s struggles with malware 20 years ago: people take advantage of an open platform, and you have to work out how far you can close the holes, how much you can scan for bad stuff, and whether you need to change the whole concept from the ground up. The answer to Microsoft’s problem wasn't Microsoft: we moved to the cloud and to secure OS models (iOS, ChromeOS). By pivoting to ‘privacy’, Facebook is trying to make the same move, but to do it itself.

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Cameras that understand: portrait mode and Google Lens

Machine learning means smartphones will (nearly) always take perfect pictures. But it also means they might understand what’s in the picture and why you took it. So what do they do with that? What does the discoverability and communication of AI look like, if you can answer lots of questions but might still be wrong?

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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 car company people look at a Tesla, they have the same reaction. 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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