This Pandemic is an Existential Crisis for Cinema

With not enough cinemas globally open to show the tentpole content, studios are electing to release on their own streaming services. This is precipitating an existential crisis for cinema. [blog.mindrocketnow.com]

During Lockdown i, you might remember that Trolls World Tour made a splash by bypassing theatres and being released on streaming services only. I remember because spending on ads on the side of buses was pretty much frozen, so pictures of those little blighters lingered for a long time. AMC took particular umbrage (possibly not at the bus posters, but at being disintermediated) and black-listed NBCUniversal. 

But Trolls World tour made $95M in 2 weeks, which compares favourably with its $90M production costs. It compares very favourably if you think that NBCU didn’t have to share any of its revenue with the theatre groups (hence AMC’s outrage). Predictably, this strategy has been repeated with other high profile movies.

Disney elected to stream Mulan on Disney+ for £20 premier access - not PPV but a one-off fee to access a premier subscription tier comprising of a single movie. It became Disney’s lowest grossing of its live action remakes to date, and won't recoup its $200M budget (yes, there were mitigating factors in the freezing of China-US relations which made this movie a cultural casualty). 

Christopher Nolan’s Tenet wasn’t released online, only in theatres, made £347M in the box office (against $205M cost). However, that box office will be shared with the theatres so WarnerMedia didn’t recoup its investment; it was also Christopher Nolan’s worst-performing movie to date. 

Mulan will be judged a success and Tenet a failure. The reason is that Mulan drove increased takeup of Disney+ (Disney won’t confirm how many people took up a Disney+ subscription and paid the premium just to watch Mulan). So the cost of the movie can be somewhat offset by a reduced cost of acquisition of the new Disney+ subscribers.

So it’s not surprising to me Wonder Woman 1984 will be released in cinemas and online on HBO Max today (Christmas Day 2020). Not only will WarnerMedia not have to share revenue, but it’ll drive more people to its OTT service (which is fourth in a field of three so really needs to catch up). What is surprising is that WarnerMedia is not intending to charge a premium for it. 

It’s conventional wisdom that even in a recession TV survives household budget cuts. TV subscription is a recurring expense, and going to the cinema is an infrequent treat. Pricing PPV as the latter rather than the former is problematic. Charging a content premium when your market is suffering from increasing unemployment is a hard sell. When the Premier League tried to charge £15 per match, fans revolted. The best riposte to this price gouging was from Newcastle United fans. Instead of paying the broadcaster, they donated the same money to a local food bank, raising over £20k. It’s not necessarily the amount, but the context.

At £20 per view, each movie will have to cost the same as Trolls to break even. The pricing looks much more compelling at £15 with a complementary digital download later, or £20 one-off upgrade to a premium subscription, as long as there’s more than just the one blockbuster. If there isn’t an upgrade fee, if the new title is adding to the value proposition for the service, then the decision is a no-brainer. 

If you’re Disney or WarnerMedia or Amazon or Netflix, the pandemic has served to increase your addressable market for your OTT service. However, it seems clear that this pandemic has hastened the demise of the multiplex. We’re not going back to a tentpole movie filling up every screen in a multiplex on opening night. Indeed, we haven’t had that in a decade. 

Cinema is no longer the premium venue to see premium content - that is now your own home. Instead, I can see chains like Everyman and The Prince Charles Cinema flourishing, spending less on screening rights, and spending more on creating an experience for cinema lovers. 

Merry Christmas everyone, and see you in 2021!

History of Media Supply Chain Workflows

Over the course of my career, I’ve changed the way I look at media technologies, as the industry itself has changed the way it implements technology. In this blog I look at the major changes of the last couple of decades. [blog.mindrocketnow.com]

When I started out in this industry, as analogue was being replaced by digital, each broadcaster had its highly secure data centre consisting of racks of highly specialised opaque boxes, all connected to each other with thick braids of cables. The role of the broadcast architect was to understand all the configuration possibilities of each of those boxes, and how to connect them into chains that represented the workflow you were trying to implement. 

The first revolution was to replace all of the highly specialised opaque boxes with general purpose computers. These were more occluded boxes, because whilst the hardware was general purpose, the software became even more configurable and required new programming skills to chain into workflows. Fundamentally, those workflows were the same, and built into the same racks, just cheaper to buy.

The second revolution is well underway, and was born of a desire to stop capital spend on new boxes every year, and to prefer annual operational spend on someone else to do that for us. So those broadcaster data centres are now all shuttered and workflows are now in the cloud. Creating the chain now only requires mastery of one or two programming languages, but also of the myriad of APIs for all the software components. And because those APIs can be improved at a much faster rate, the possibilities to do interesting things in those workflows has increased at a much faster rate also.

At its simplest level, the workflows have remained the same throughout all of these three revolutions. For the purposes of this post, I’m going to focus on VOD for TV, and the playback workflows within that. (I’m not going to get into associated and important workflows like marketing, revenue assurance, operations.) Let’s get into it!

Simplified broadcaster workflow

The role of a broadcaster is to market their catalogue as widely as possible. To do this they focus on making programmes applicable to the market:

• Choosing titles that will be popular, then putting the corresponding master assets into the workflow

• Making sure they conform to technical quality standards

• Making sure that they conform to legislative taste and decency standards

• Enriching with metadata (enrichment assets) that makes the asset more attractive, such as creatively written synopses, artfully composed imagery, dubbing in local language by local talent, creatively written subtitles

• They also look at enrichment in a broader sense, such as marketing titles locally in cooperation with service providers

• Insert ads

• Finally, the asset is exported in a form that can be imported by service providers, at negotiated cost, at an agreed schedule

Simplified service provider workflow

Service providers are sometimes the same broadcaster that provided the VOD asset, other times they are MVPD (multichannel video programming distributors), or perhaps content aggregators. All have a similar role to broadcasters, to market their (aggregated) catalogue (to consumers) as widely as possible, with the addition of implementing an attractive business model:

• Making partnerships with broadcasters that are popular

• Making sure the VOD assets that are provided conform to technical quality standards, and are timely

• Further conform the VOD assets to conform to platform technical standards, e.g. different bit rates, packaging

• Insert/ replace ads

• Enrich the metadata to reflect the business model, including: DRM method, tagging for revenue accounting

• As each consuming device might have a different preference for how it consumes the asset, the asset is exported once more

• Ingest the associated metadata (enrichment assets) into a catalogue and making it discoverable

• Promote the title both technically e.g. on the home screen of the app, and also non-technically e.g. in marketing literature

Simplified consumer device workflow

In many respects, the end consumer holds the power in the value chain. They determine the value of assets by asserting their choice of what to watch. They also provide one of the two sources of money in the value chain by buying stuff. To do this, their workflow consists of:

• Discovering content to watch, which is usually done by viewing associated assets, e.g. the next programme in the programme guide, “watch next” recommendations, marketing literature, ads

• Buying the content (even free content goes through the same authentication/ authorisation/ accounting process)

• Playing the content (which encompasses the technical processes of unpacking, decrypting and presenting images)

• And I’ll separate out viewing as oftentimes the viewing is on a different device to the playback, which can also insert/ replace ads i.e. smart TVs

• Watch the ads, watch the content

What next?

This business has changed dramatically over the last couple of decades, unsurprisingly as technology as a whole has changed dramatically. Broadcast generally follows, not leads, and at a comfortable distance, in order to sweat every last drop of amortisation from investments. So it’s relatively easy to make predictions - just look at what’s going on elsewhere. Here are a few of the trends happening in other industries which will wind their way into broadcast at some point.

Intelligence at the edge

A relatively new website architecture pattern is the Jamstack (JavaScript + APIs + Markup). The idea is that the website application itself is static, and as much intelligence as needed is associated with the content. That means the website can be distributed over a CDN and doesn’t need to be built for each visitor individually.

For media workflows, as some of the intelligence can only be implemented when you know the target, it means pushing out some of the conform/ enrich/ export steps from the central cloud to the network edge. This becomes possible as CDN providers implement cloud processing features at cloud prices.

Dumb apps

Putting intelligence at the edge means being able to deploy dumb apps. Maintaining apps fast becomes a profusion problem. The Now TV app is available on Apple, Android, Xbox, Roku, Samsung smart TV, LG smart TV, Amazon Fire TV as well as its own range of hardware. Each of thes hardware platforms comes with their own app store, and each app store has its own foibles. Each release can take 2 weeks to approve, so many companies employ release managers to handle the negotiations with the app stores to assure that they will pass first time. Even so, it takes a lot of resource to manage 8x approvals in parallel.

It’s very attractive to only need to deploy a new release once a year, to reduce the app store submission headache. This requires finding clever ways of making the app dumb. Web browsers are very complicated pieces of software that are almost invisible to surfing, and this is the direction that video apps need to go.

Intelligent content

As the app gets dumber, the content will need to get cleverer. Entire features of the app will need to be delivered as content. Content will self-aggregate as web pages with hyperlinks can, so that enrichment assets can be viewed at consumption time. And content will change after publishing, no longer remaining static. Just like the recent Cats movie, you’ll be able to see version 2.0 of your favourite movie as it reacts to real audience reaction.

What do you think of my predictions? Let me know in the comments!

Controlling the Algorithm

Can algorithms be racist? If they can, how can we control them? In this post, I look at the presentation of bigotry in technology and what we can do about it. [blog.mindrocketnow.com]

As part of my Python course, I’ve been learning machine learning techniques, or how bots recognise patterns in data sets by calculating correlations. In other words, how to create a decision-making algorithm. When I looked up from my keyboard, I began to notice that algorithms are getting some very bad press at the moment, which made me think a bit deeper. 

History of bad ideas

Facebook is suffering from more sponsors withholding ad spend. Despite Facebook claiming today “There is no profit to be had in content that is hateful”, the company still cannot stop big brands’ ads being placed next to racist posts. Big brands are responding in a way that’s eye-catching, by withholding ad spend. Eye-catching, but perhaps not ultimately effective as just 6% of Facebook’s revenue are from big brands. The remaining 94% consists of hundreds of thousands of businesses around the world, who cannot afford alternative methods of reaching their audience. The extraordinarily broad success of Facebook’s algorithm emboldens it to be blasé to both government and big business despite civic and corporate activism. And Facebook has some truth on its side, as its algorithm wasn’t designed to promote racism, so how can it be responsible for the racist outcomes?

Perhaps you remember Microsoft’s Tay bot, born in 2016. Within 16 hours it became a staple of future AI courses as a cautionary tale. Unwisely, Microsoft designed Tay to learn language from people on Twitter, as it also learned their values. Trolls targeted the bot and trained it to be a racist conspiracy theorist - presumably just for fun. Which essentially how trolls birth other trolls in their online echo chamber.

Things haven’t improved over time. Let’s try an experiment together right now. Perform a Google image search for unprofessional hair. What do you see? I did that just now, and saw pictures of mostly black women, which infers that most women with unprofessional hair are black. Which is racist. Was the algorithm that presented the results racist?

Enough people labelled pictures of black women with “unprofessional hair” that Google’s algorithm made the correlation and applied that inference to all the photos that it came across. This news story first broke in 2016. Before then, you only saw pictures of black women. Now, you see news stories interspersed with pictures of black women. Which shows that Google’s algorithm can’t distinguish between the two types of results.

It’s worth repeating: the algorithm cannot differentiate between non-discriminatory stories about discrimination, and results that infer discriminatory conclusions. This is because digital footprints never go away. Digital history is only additive. But at least whilst the search algorithm reductively simplifies and generalises, it doesn’t confer moral value, as both types of results are shown together.

It occurs to me that this is no different to people simplifying and generalising. But people normally understand that individual interactions are nuanced and to be judged on their own merits. Algorithms inherently do not. And it’s so much worse when algorithms enable ill-judged conclusions because they’re so impactful when they get it wrong. Algorithms now control all the complex transactions in life:

• Presenting options for what watch next in YouTube;

• Adjusting your insurance premium based on how hard you brake and accelerate;

• Sequencing traffic lights in city centres;

• Filtering your CV for keywords, to see if you’re a good candidate to interview;

• Then assessing you might fit into company in your first video interview by measuring how you fidget;

• Analysing credit card transactions to spot fraudulent activity;

• Predicting crime hot spots based on the wealth of neighbourhoods;

• Spotting the faces of terrorists flagged on watch lists on public transport CCTV.

Correlation is not causality

The core of the problem is that algorithms present correlation, and we interpret them as causality. Which at best is spurious, and at worst is bigoted. Algorithms aren’t inherently problematic, but can become so because people are, and the algorithms learn from people. The results aren’t inherently problematic, but do infer problematic conclusions, if we don’t understand their limitations. This train of logic is how we end up with discriminatory health insurance pricing.

Algorithms can be inspected, whereas humans cannot. But let’s not mistake this transparency for understanding. Even if the algorithm itself is clear and concise, the data sets are often complex, which makes outcomes unpredictable and not understandable. Then because we don’t understand them, yet believe that some other smart person could if they wanted, we over-trust them. There’s no civic demand to examine them, and civic acceptance of the conclusions. Which is how we end up with law enforcement resourcing algorithms over-policing poor neighbourhoods, and becoming part of the problem. Funding according to the algorithm targets the correlation, but not the cause.

So we have seen how spurious correlation and inherently biased data sets are major weaknesses of algorithms. The third major problem is that algorithms use past data to make predictions on likelihoods of decisions. As every investor knows, past performance does not necessarily predict future results. Decisions based on likelihoods are very bad at figuring out what to do in edge cases. When you apply those algorithms to millions of decisions, the number of bad decisions at the edge mount up. And each of those bad decisions changes someone’s life. Each edge case matters to someone.

It’s beneficial to allow people to game algorithms? For instance, part of the duty of hospital administrators is to work the NHS appointments system to enable patients to reorganise treatments for the convenience of the patient. Human intervention is needed because the appointments system optimises hospital resources.

Last Monday, TikTok and K-pop fans claimed responsibility for the lack of supporters at Donald Trump's campaign rally. They understood how the TikTok algorithms boost videos in order to promote them to like-minded activists, and deleted their posts after a day or two to avoid the plan leaking to Trump’s team.

The alternative to not understanding these algorithms is no longer feasible. The world has become too data rich to be navigated without help from artificial intelligence. We can either manipulate them to our benefit, or they will manipulate us to theirs. So what can we do about it?

Legislation and activism

It’s not illegal for a business to prioritise its resources to serve customers that are willing to pay the most. For example, it’s not illegal to prioritise call centre agent pick-up times based upon whether your number matches a list of high-value customers, even if it means you don’t answer calls from low-value customers at all. But it is discriminatory. 

In the EU GDPR legislation gives citizens the right to explanation of automated decision making. On request, companies are obliged to explain how sharing data links to decisions made about customers, and the impact of that decision. This lays important legislative foundation, as it forces companies to understand how data links to decisions, which many do not. However, this legislation falls short of protecting against bad algorithms, data and decisions. 

As we’ve seen, the combination of complex logic, inherently biased data sets and prescriptive application, make algorithms overly blunt instruments. This is now recognised by leading tech, perhaps more so than governments. Amazon notes that technology like Amazon Rekognition should only be used to narrow the field of potential matches, but because legislation still doesn’t understand this, it is implementing a one-year moratorium on police use. Amazon recognises that asking individuals to safeguard themselves against mis-application of its algorithms is unfair because it’s just too complex for end consumers.

This is exactly where we need legislation to protect us. My hope is that parliaments will enact well-considered limits on use of algorithms in industry and government, focusing on public safety and law enforcement. We should then use these limits to hold companies and agencies who do not safeguard their algorithms to account.

Legislation is not the only only tool that we have. As I’ve noted early, I do agree that inspecting algorithms and data sets is out of the reach of all but data scientists, so useless to most agencies. However, specific testing for discriminatory outcomes isn’t. So another important tool is to empower trading standards bodies to test for algorithm bias, the same way that they test for food hygiene.

Finally, there are things that end consumers can do to train the algorithms. We can increase our social connections, because online segregation is as destructive as physical segregation. By exposing ourselves to more opinions, algorithms are exposed to more diversity and present less echo chamber click bait. 

We shouldn’t engage with the click bait, the posts that elicit strong emotion. Imagine if the whole world scrolled past the trolls - then the oxygen would be removed, and the trolls would wither away, because the algorithms would see that bilious content is not clickworthy.

Fundamentally, we should play nice so that algorithms don't make bigots of us all, otherwise we only have ourselves to blame.

5 Tested tips for remote working

How did your first week of working from home go? These are the tips that worked for me - let me know which ones work for you. [blog.mindrocketnow.com]  

In this time of global pandemic, most of us are being asked to work from home. Here in England, we’ve just finished our first week where the kids have been schooled at home, so the first week of all of us working from home at the same time. I was prepared for a disaster caused by overly overlapping personal space - but it seems to have worked out well (so far). Perhaps this is why:

1. Make sure your environment works

Near the top of my personal irritations is the consistent loss of the first 7.6 minutes of each meeting with “can you hear me?”. All too frequently, the meeting is abandoned entirely. Seeing as it took so long just to get the diaries aligned just to get this meeting slot, I inevitably fall back on email and slack messaging, with all the communication debt that incurs.

Much better is to set up your environment in advance. Start with the basics: are you sat at a desk in a room where the door closes? Do you have a good quality speaker or headset? Does your software recognise the webcam? Is your bandwidth high enough for all of the household to have conference calls (house parties) simultaneously? Have you taped shut the door of the microwave?

Do you have the right hardware? We went with iPads + bluetooth keyboards for the kids, and laptops + peripherals for the grown-ups, because that’s what we were all familiar with. Familiarity means self-troubleshooting and not yelling for Daddy for tech support.

Then there’s the software. Every participant in a call needs to use the same software, and have it installed ahead of time, then add the other participants to their app’s address book. Some free software has a limit on the number of participants in video conferences. Some free software defaults to open conferences that anyone can attend. Finally, and before your call, test your setup beforehand. 

Even after all your preparedness, the call will still lose time to people sounding like a Dalek, but at least it won’t be you.

2. Commit to a routine

The psychological cues that come from a routine give you a short-cut to productivity. That’s why uniforms exist, and why you only seem to start thinking whilst lacing your shoes. It’s why the evening commute helps to bookend the day, to mentally check out. We found that we needed to replace the normal physical cues with other physical cues. And when we didn’t, the morning seemed to evaporate without anything productive to show for it. My ideal morning routine consists of setting out my daily intention, meditating for 15min, and doing some light physio for another 15min.

Speaking of mornings, it’s true for most of us that this is peak productivity time, so attack your most difficult or intricate work items then. However, you probably still need a simple first task to work through the mental gears and get into the flow - which isn’t making another cup of coffee.

However, making a cup of coffee is important for a few reasons: for breaks, for hydration (though water is clearly better), and as a reward for your good behaviour. So make plenty of coffee (or better, red bush tea). Counter-intuitively, breaks to the routine support the routine. So at lunchtime, don’t feel guilty if you watch some Netflix. Make sure you go outside for your one walk of the day, and go every day, rain or shine - the change of scenery really is as good as a rest.

Clock out at the end of the day, as you would during the evening commute. We found sitting round the kitchen table for tea and cake, or watching an episode of The Simpsons together, or going for a walk, was a clear way to end the day, and removed the temptation to keep an eye on the email.

Finally, the weekend isn’t a reason for pausing the routine. We found we still need the morning physical cues, even if the day consists of different activities. When we didn’t, me and the eldest found ourselves sleeping the morning away.

3. If one person works remotely, everyone needs to behave remotely

BP (Before Pandemic), it was very easy for the single remote worker to feel left behind. Many decisions were made in the office kitchen, information was disseminated over email, status was shared verbally. Remote team members were filled in later. Team empathy was fostered by going to the pub afterwards, by those who happened to be in the office to be rousted. Now everyone is a remote worker, the playing field is levelled, and everyone has to try harder.

Everyone logs into video calls, so everyone should adhere to VC etiquette: test your kit before the call; latency means don’t interrupt; being a small face in a grid means making bigger gestures; presentations need to use bigger fonts; showing the background of your home office tells people about the non-work you. And always share video, it’s so much more effective than audio only.

Working across time zones is hard. In my previous job, I had to schedule calls with folks from Buenos Aires, Singapore and London, and it was always the Singaporeans who seemed to need to work late. Companies like Trello institutionalise common working hours of 12-4 PM EST regardless of your actual location. But for the majority of the time, you’ll be working asynchronously, so your communications needs to support that.

I found that the quality of knowledge sharing is really put to the test in remote working. Knowledge needs to be searchable rather than gained by knowing the right Slack channel or the right person to ask; it takes too long to absorb collective memory verbally. Knowledge should be openly shared, rather than restricted in an email distribution list. Decisions should be archived effectively, not hidden in status reports. 

All communications are now digital, so there’s no reason not to include everyone. But rather than broadcasting to everyone just in case (= spam), it should be the responsibility of the remote worker to subscribe to the right channels, to not be left behind by omission. It’s also the responsibility of remote workers to remain current; skim all the channels, read the status decks, attend the stand-ups, schedule 1:1 calls with managers and peers.

Digital tools make presence much easier. It’s now trivial to signal whether you’re open to informal contacts, open to meetings, or blocked for focus time, or blocked because you’re not working. On the other hand, digital tools make it easier to flood communications, so it’s important to choose the right tool when giving and organise when receiving.

4. Over-communicate

You’ll doubtless read about the importance of over-communicating, that if you think you are over-communicating, you’re probably only doing the right amount. We found aspects of over-communicating to be important, but to be treated with caution.

Our children are very clear about the difference between right-sized communication and over-communication. They like being set specific, measurable, attainable, realistic, time-bound school work, because it gives them the certainty of knowing when done = done. But they react badly to unfocused, unclear, repetitive communication = “boring”. They resent being asked for status as a proxy for justifying their time (which is why “how was your day?” yields a monosyllabic “fine”), but are very happy to share status when it’s truly sharing (which is why our game of “tell me just one thing” works well at the dinner table).

Over-communication amplifies the difficulties with digital tools. Non-verbal cues are missing in most digital means, so it’s important to assume positive intent both in giving and receiving. The digital cues are different; @all and @here shouldn’t be abused as they signal that what you have to say is important enough to interrupt everyone else’s train of thought/ dinner.

It seems to me that the secret of successful communication is to treat all adults like children, and all children like adults.

5. Hold yourself accountable

Personal productivity requires personal accountability. Unless you’re clear about what you will do in any one day, and more importantly what you won’t, then you’ll never be finished. Our children have timetables from their school to give them their structure. I prefer the GTD method to give me the boundaries to delineate work from home. Without it I find it too easy not to start my day because I don’t have a simple start-up task defined, and I find it too easy to continue thinking about my email when I should be listening to a family member.

Unless you hold yourself accountable and are proactive, you will be left behind. People won’t reach out to you if they don’t know you’re there. So I’ve learnt to make a little noise: ask questions in the work topic Slack channels, and contribute nonsense and gifs in the social channels.

Holding yourself accountable is the difference between receiving direction and choosing direction. For my children it’s the difference between disliking a subject because they’re not receiving teaching that they get on with, and liking a subject because they’re learning for themselves.

Bonus: 3 things not to do.

Of course things have gone wrong this week, and this is generally because the same challenges to team dynamics in the office apply to remote working.

It seems to be harder to create an environment of psychological safety when working remotely. To create this safe space, to create team empathy, don’t be all about work. Find a space (perhaps in the 7.6min of “hello, can you hear me?” at the beginning of each call) to share geographical, cultural and personal contexts. Be interested in other people. And watch out for your unconscious biases - if there’s someone who’s habitually not “on the same page” as you, think about why. My wife has virtual office drinks at 5pm on Friday (neatly circumnavigating the no booze in the workplace rule) which strikes me as an excellent idea.

Don’t try and multi-task. Just because your computer screen can show two things simultaneously, doesn’t mean you can surf the web whilst on that conference call. You can’t do useful office work if you have to babysit your toddlers. You can’t do more than triaging your email whilst waiting on hold to the doctor’s surgery. Be present and engaged when you work, because this will give you the head space to be present and engaged when you’re not working.

And because things never go according to plan, don’t be too hard on yourself if you need to change. Go with it. The rewards are well worth it.

Thoughts from the DTG Summit 2017.

The broadcast industry is catching up with the world around it. It’d better hurry up. blog.mindrocketnow.com

The DTG Summit is one of the smaller conferences, catering to the UK broadcast community. Even though it’s small, there’s a good mix of broadcasters, technology vendors, service providers, and regulators, which usually makes for a good spread of perspectives. This year, they all seemed to converge on a couple of ideas: 1) IP is a good thing; 2) Netflix and Amazon have a lot of money.

A climate of fear

However, both of theses ideas were expressed in a way that fed a pervading sense of fear. Fear of being forced to spend money on newer shinier kit, fear of other entrants taking market share, fear of losing revenue, fear of the world moving on whilst broadcasters ossify.

These are all real, legitimate fears, founded in established market trends. For example, the only revenue growth in Pay TV appears to be from the lower spending homes. For example, the most popular UK TV content is still free, so there is a large constituency of TV viewers who are happy with not paying  – even if it means missing out on the latest features – so there is a large legacy viewership to keep happy. For example, investment cycles by TV operators is still around 5-7 years, compared with software app cycles of 1-3 months, which makes it very hard to TV technology platforms to keep up with best in class software.

Disruptive effect of IP everywhere

Broadcasters have been late to implementing IP end-to-end because they’ve been trapped in their decade-long investment cycles. Other sectors like banking have come to grips transforming their infrastructure to highly available, highly secure, highly flexible, all the while reducing operational costs – through implementing IP everywhere. (The banking sector has spent a huge amount of money to get here, but the point is, they are already reaping rewards.) The broadcast sector is just starting to link its digital islands, and is finding it very hard and expensive to do so.

Whilst IP everywhere will undoubtedly lead to improved operational efficiencies, that isn’t the disruption being caused in its wake. The disruption is a consequence of the cost. The irony for me is exactly what the broadcast industry finds expensive and hard, has actually made it cheaper and easier for new entrants to come into the marketplace. The barrier to entry into the broadcast space has been lowered because: you don’t need as much scale to be economic since the incremental cost of IP delivery is negligible; new opportunities can be trialled without needing to underwrite/write off large development costs; geographic location is truly irrelevant opening new opportunities for resource arbitrage.

To put it another way, newbies can find value with IP everywhere in not only doing new things, but also doing existing things because they can do it cheaper than broadcasters. The solution for broadcasters appears simple to me: get on the IP everywhere train as soon as possible to remove the cost disparity and to find new value opportunities. It’ll cost a lot to implement, but it’ll cost a lot more to go slowly out of business.

Disruptive effect of new money

Despite a pervading air of fear, the conference never fails to self-congratulatory over UK’s talent in media and entertainment. UK broadcasters may not have the wherewithal to spend as much as Netflix on new content, but at least every villain is British. UK broadcast punches above its weight.

My problem with this contention is the aspiration to spend as much as Netflix. For a start, Netflix and Amazon together now spend more on creating content than all the Hollywood studios combined (around $12B). We don’t feel the need for BBC or ITV to be as big as Disney, so perhaps we shouldn’t feel threatened by Amazon.  My other problem is the implicit correlation between spending money and creating quality programming. Whilst it’s true that the more money you spend, the bigger the sets you can build. It’s also true that there is plenty of quality TV to be found in niche broadcasters like Channel 4.

For me, the disruption isn’t the fact that there are new entrants with deep pockets. The disruption is the fact that these new entrants have ability to move the market. For example, Netflix is pioneering the use of data and analytics in its delivery of content. For example, Amazon has implemented voice search successfully for TV (and I think this will be the interaction mechanism of choice with TV viewers within a couple of years). For example, Netflix launches entire series in one go, a practice that is now common amongst all providers.

It’s also a fallacy that these new entrants have a money-no-object approach to new technology. They have deep pockets because they’re successful, not because they’re profligate. It’s this success that we should emulate, not the spending.

A future of opportunity

These musings may sound gloomy, but I’m actually very upbeat. I came away from the conference with a renewed sense of confidence. There’s a key advantage to being a second entrant into a market – others have created that market space for you. There’s already a clarity of vision, and someone else has shown all the steps (and missteps) to getting there.

Broadcasters can learn from the many lessons from other industries, and from first movers. Learning these lessons should reduce the inevitable cost and effort required to re-platform an entire industry onto IP everywhere. Learning these lessons will help to create industry-leading content without industry-leading spend. The opportunity is to learn from the pioneers and to do it better.