What’s so good about growth?
I’ve been running a workshop for an amazing group of New Zealand filmmakers: http://compton.school/the-work/partner-workshops/. The theme of the workshop is growth and I kicked off with six good reasons to grow your business, drawn straight from the textbooks:
1. Economies of scale and scope. Things get easier and cheaper when you do more of them.
2. Specialisation. Individuals can focus on the things they do best.
3. Bargaining power. Size commands respect.
4. Smoothing. Bigness smooths out revenues and risks.
5. Resilience. Team spirit sustains the enterprise.
6. Visibility. You get noticed.
Proving the power of the group, we came up with six more:
7. The information. More eyes and ears.
8. Trust. People place more trust in bigger entities.
9. Accountability. Responsibility to others brings more discipline.
10. Bigger projects. Ambition is enabled.
11. More ideas. More minds, working together.
12. Completion of the team. No gaps.
I’m not sure I swayed everyone but it’s a pretty good list.
Power: given or taken?
Fans of The Godfather and students of Machiavelli know that real power must be taken, not given. It’s a brutal vision of the world, life at the top table, a fierce contest for whatever spoils are judged the most valuable.
Yet most of us play a different game. A closer contest, less fatal, more open-ended. We know that power is something we mostly hold at the pleasure of others.
If you are playing this different, more open-ended game you might be interested in the work of psychology professor Dacher Keltner. His book The Power Paradox sets out the rules of the game in 20 principles. As a taster, here are the first five:
#1 Power is about altering the states of others.
#2 Power is part of every relationship and interaction.
#3 Power is found in everyday actions.
#4 Power comes from empowering others in social networks.
#5 Groups give power to those who advance the greater good.
We’re a long way from Machiavelli and quite some distance from the operating premises of most of the leadership literature. We’re in the everyday world of ordinary people and the interplay of ordinary business, carried on without the sanction of violence.
Of course it’s not all sunshine and light. Keltner’s last 8 principles are all about the downside: incivility, threat, powerlessness, adverse health effects.
But the model I think is helpful, particularly for people working in the creative economy, where persuasion and teamwork are the everyday mode.
Gamekeeper turns poacher
Ex Droga 5 CEO Andrew Essex has written a book called The End Of Advertising. His epiphany and resulting departure from the ad industry – he is now CEO of Tribeca Enterprises – came when he learned about ad blocking and instantly converted. And like any true convert, he fell hard: ‘One day, we’ll look back on the fact that we forced people to watch ads with the same incredulity we reserve for, say, smoking cigarettes or wearing fedoras. Perhaps the most enlightened brands should start thinking about reparations.’ The book is repetitive and longer than it needs to be but definitely worth a look. What can advertisers do now that audiences have the power to zap their ads? Get more creative, Essex says. ‘And by creativity I don’t mean simply making less annoying ads, which are always welcome. The real goals are big ideas that reinvent or fully replace ads.’ As examples he cites The Lego Movie – a hit movie ‘that also happened to be an ad’ – and Citibank’s Citi Bike program in New York City. The book ends with 10 principles for better advertising: in brief, ‘Adapt or Die’.
AI breeds more nimble, fan-driven media
NYC Media Lab has released a white paper about the influence of artificial intelligence on media economics. Some conclusions: 1. Rapid audience feedback means media ‘can take an iterative approach, test more, and improve faster’. 2. ‘Media brands will invest in technology to attract fans, not casual viewers.’ 3. Media employment won’t shrink yet, but production roles will change.’ To download the paper visit –
Wagging the long tail
Chris Anderson excited a lot of interest when he argued back in 2006 that movies, books and songs in the ‘long tail’ – ie at the tail end of the sales charts – could collectively equal or outsell the hit movies, books and songs at the ‘head’ of the charts.
What powered Anderson’s claim was the increasing importance of online sales, where inventory and transaction costs fall away sharply, allowing platforms like Amazon and Netflix to offer people literally millions of titles.
Anderson’s claim has been contested, notably by Harvard’s Anita Alberse, who argues that the internet in fact magnifies the returns to blockbusters:
There’s an excellent discussion of the rival claims in Michael Smith and Rahul Telang’s book Streaming, Sharing, Stealing (see chapter 5 in particular):
Smith and Telang argue that the long tail is a whole new business model quite different to the blockbuster model perfected by the record companies and Hollywood studios:
‘Long tail business models use a very different set of processes to capture value. These processes—on display at Amazon and Netflix—rely on selection (building an integrated platform that allows consumers to access a wide variety of content) and satisfaction (using data, recommendation engines, and peer reviews to help customers sift through the wide selection to discover exactly the sort of products they want to consume when they want to consume them). They replace human curators with a set of technology-enabled processes that let consumers decide which products make it to the front of the line. They can do this because shelf space and promotion capacity are no longer scarce resources.’
Clearly what matters here is technology – the internet itself and the algorithms that help people find what they want. And it is these algorithms, so-called machine learning algorithms or ‘learners’, that are driving the rollout of streaming services like Netflix, Spotify, Amazon and locally, Stan.
Machine learning then is the engine wagging the long tail of audience choice and the emergence of ‘niche’ blockbusters like House of Cards, Top of the Lake and Wolf Creek.
Machine learning expert Pedro Domingos sums it up:
‘In retrospect, we can see that the progression from computers to the Internet to machine learning was inevitable: computers enable the Internet, which creates a flood of data and the problem of limitless choice; and machine learning uses the flood of data to help solve the limitless choice problem. The Internet by itself is not enough to move demand from ‘one size fits all’ to the long tail of infinite variety. Netflix may have one hundred thousand DVD titles in stock, but if customers don’t know how to find the ones they like, they will default to choosing the hits. It’s only when Netflix has a learning algorithm to figure out your tastes and recommend DVDs that the long tail really takes off.’
To learn more about machine learning, have a look at Domingos’ book:
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