One of the challenges of a co-operative is balancing the interests of the group with those of each individual member. Unlike a proprietary limited company, stakeholder interests are not beholden to the profit motive. Rather the primary focus is on return to members – which can be delivered in a variety of ways including via dividends, rebates and patronage. The difficulty is that without a return on capital signal, the cooperative itself has to determine how value is distributed among its members.
A data co-operative provides a simple illustration of this challenge. On the one hand, members come together to aggregate market power – as individuals they are weakly positioned to bargain the terms on which they share their data, while as a collective their individual control is amplified. On the other, one of the principal objectives of a data coop is to enable individual members to have more control over their data. For this reason, a data coop will create permissioning mechanisms that enable the individual to differentially share their data with others. The data coop leverages group market power to deliver value for individual members.
But that is only part of the story. By aggregating control over data, the data coop will also enable sharing of data in aggregate. And there are likely to be circumstances where it is in the collective’s interests to share aggregate data. This is after all why Big Data and the Algorithms have been dominating the pop charts over recent years. For this reason, it may be a condition of membership that coop members agree to authorising the coop to share their data. You can see the challenge here…
For this reason, it is really important that cooperatives are very clear about how a cooperative distributes value amongst its members. If interests are not aligned, fractures across the points of difference are likely to occur when the structure is stressed.
Annie Duke, retired poker champion, talks about strategies to accelerate learning in her book “Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts”. A key one is drawing on your community – learning from the experience of experts, testing boundaries with peers, and simplifying ideas through teaching others. Really interesting question here about how to institutionalise these types of practices within an online cooperative community…
A data coop by definition aggregates the market power of its members. It enables them to collectively negotiate how their data can be shared and used by others – as well as enabling each individual member to manage how their data is shared. We think that this is key to the success of a co-operative – they empower the collective while also enabling individual agency.
It is kind of obvious, but can get lost in the mix, that for a data coop to work it must also benefit the customers…
I had a colleague once who for a time was the leading lights on the investment banking industry. He was in his early 40’s and was smart and full of confidence. Similar to a lot of his caste, his sense of the public good was tied to his power not in giving.
As a banker at the height of his game, he attracted customers like moths to a flame. It helped that he was in the real estate financing business and this was in the lead up to the GFC. As it turned out, he was instrumental in creating some of Australia’s biggest financial disasters that were to come, though he would never be held accountable.
So I remember in this one meeting with a property development company that was eager to float into the all too hungry oceans of investor capital. They had been granted an audience with my colleague who had sat quietly through their presentation whrre their sought to explain what they wanted to do. At the end, he approached the white board, drew three boxes and a couple of arrows and then explained how one plus one could equal three. This was the strategy he would recommend. And with that he finished with the line “You don’t pay for my time. You pay for my expertise.”
Now that is a very simple way of saying I’m bloody expensive. But is it actually accurate. After all, time is ultimately all we have to sell – particularly, when you are an investment banker who doesn’t physically produce anything tangible. It’s bothered me ever since. What was he actually saying?
My take on it was about positioning. His time was so precious that it looked damned silly as an hourly rate. He was creating scarcity around his brand. He had collected expertise and more importantly a network that he could call on to execute the transactions that could deliver them their dream outcome. He only needed 2 or 3 clients cause they were elephants that were totally aligned with him – they made more money, the more money they raised.
It’s a clever approach for a consultant, or anyone in the business of selling their time rather than a product (that could well be an asset created by an investment of their time). Professional sportsmen are faced with the same challenge. Their sunk costs are into themselves, they and their parents have invested years in creating their performance edge. And they are so acutely aware that their time at the top is finite.
One of the benefits of our social media age is that democratization of personal brands. With control of entertainment and information flows having been lost to the masses, the ability of just about anyone to rise up from the cacophony has been made possible. Not that it makes it any easier. The competition for attention has also been amplified by a factor of a large number.
This is the reason people like Seth Godin are so strong on recommending that you focus on your tribe- that group of people outside of your family and friends that will like your story. The more narrowly defined your tribe, the more likely that you can hone your message to resonate with them. It’s the same way that Kevin Kelly arrived at the conclusion that all you need is 1000 true believers to make a living.
So the lesson is if you are in the business of selling your time – which inescapably we all are. Then you better start by focusing on selling your message to a well defined audience. The tighter the framing, the more likely that you can tailor it to their needs. So much the better if that audience has very deep pockets, or in the case of my banker buddy, access to truckloads of other people’s money.
Where once we had red phones, we now have red buttons. The imagery of nuclear negotiation has morphed to reflect the times. When deterrence is based on mutually assured destruction, the need for direct negotiation to avoid misconstrued emoticons is paramount. But with North Korea the destruction isn’t assumed to be evenly shared – hence the bigger button – though perhaps the better image is a battery of buttons (or is the collective noun a ‘minefield’?).
Just how the US imagines it can prosecute regime change is the realm of Monte Carlo simulations. We can be sure that they have their plans, and that they are unlikely to include nuclear weapons – except as a retaliatory contingency. So what can we learn from negotiating nuclear-style?
Power asymmetry isn’t all it cracked up to be – Can the US really sustain an atomic explosion on its landmass? Can the US really justify total annihilation of the North in response? These are the implied positions – neither seem particularly sustainable. The implication is that bargaining from a position of relative strength doesn’t help, if the ramifications for any action using your strength are likely to be poor. There are no good nuclear wars.
You go first – in a gunfight, it’s the fastest draw that wins. It doesn’t work that way when both sides are missing body parts when the dust settles. The incentive is to avoid action regardless of your relative strengths. In these circumstances, the US has little leverage to force the hand of North Korea to abandon its program as it is caught in a ‘you-move-first’ bind.
How to make a credible threat – notwithstanding the above, no one wants to cross a crazy man. It’s the reason ‘white with anger’ is so much scarier than the everyday red-faced variety. In the game of bluster and bad behaviour, President Trump has demonstrated an expertise that none of his recent forebears could match. He is a better actor than Ronald Reagan and has none of the restraint or class of Obama. The thinking behind escalation strategy is to automate the launch process beyond a trigger point – midnight on the Doomsday clock. Trump’s crazy eyes and small hands are all designed to blur his fingers on that trigger.
Where’s the escape hatch – from a North Korean dictator perspective, there doesn’t seem to be an option that leaves me alive – let alone saving face. Given my demonstrated disregard for the people of my country, is it really likely that I’m going to get altruistic at the very last moment? What I need is a way out – short of being relocated in a witness protection program. Does the North become a vassal of China? At least that way, the nuclear codes join a bigger library.
Coalitions still win – which leads to the real problem with the current US strategy. Coalitions win conflict. Remember the invasion of Iraq, there was a whole sham thing about weapons of mass destruction? This time there can be no doubt about this. So why isn’t there a mass movement to de-arm?
Nemo was a special snowflake. Unique just like all the clownfish that had come before him. That is the way evolution works – flawed replication fidelity. Does that make him important? Well, yes to his dad at least. But that is different from the sense of entitlement that comes with being told as a peanut that he was destined to change the world. Not every clown fish can make a dent in the universe. So how did he do it?
Consider again the school of fish. By definition not everyone can be the leader of the pack. If you are a fish that is travelling in the middle of the school it is nigh on impossible for you to go in any direction other than the same one as those around you. If you try to deviate, you’ll bump into your neighbour and they are in the same boat as you. You may as well be travelling in a bus that someone else is driving.
To lead the direction of the school, you really need to be on the edge where you are responding to the environment external to your school. And to be a little clearer, its better to be towards the leading edge of the school.
To swim at the leading edge is to be exposed. The risks to your wellbeing are heightened. The protection of the herd is a function of you being the first to fall – like a centurion in the front line of the phalanx, your role is blunt an attack and give those on the inside time to respond. Whether you survive is as much down to luck as it is to good management by you.
And what does management mean in this context? Your options are limited. You make a difference at the edge – literally. You are subject to the same inertia as those in the middle. The difference is that you can try to change direction. Say you are the first to spot a very large set of teeth bearing down on the school. You try to change direction and bump into your neighbor. In a fright, they see what you see and turn the same way you are trying to go. They bump into their neighbor and so on.
You can see where the idea of a tipping point comes from in network effects. A small and determined group of fish can turn the direction of the herd. They must for the survival tactic of herding to work.
So some lessons in leadership from the school:
It’s a risky endeavor, whether you succeed or fail is a function of luck as much as determination
Inertia is your friend…eventually. It’s not like turning the oil-tanker, you have to convince a few before you can convince the many. You are looking for a tipping point in order for the herd to respond
Find the front, that is where current trends are taking everyone. From there you can make your move.
The alternative is to swim off by yourself and live in a cave. Nothing wrong with that. Plenty of fish have done it and lived out their days in existential bliss. Just don’t expect to impact the herd from the safety of your cave. They are off in the depths doing their own thing.
Ever been culpable of rolling an idea up a hill beyond its use-by date? Even with the benefit of hindsight, it can be kinda difficult to spot the optimal moment when it would have been wise to let go. Which is why this ‘fail fast’ fad has me flummoxed. I’d hoped that I could apply it to experience and not repeat the mistakes of the past, but I’ve been struggling to understand what it actually means.
See, I first took it that decisions to abandon a project can be accelerated. The lean model talks about testing your hypotheses and quitting as soon as they fail. So failing fast becomes about setting specific and measurable benchmarks that trigger the escape hatch if they are not met.
A problem with this is that benchmarks may not be simple to define and can be harder to measure – particularly where the concepts are emergent. For example, how do you define a benchmark to filter out the positive responses in market testing that, combined with a little confirmation bias and a dash of inertia, provide more than enough motivation to keep going.
Another assumption is that you can save money, time and effort by stopping out of failures early. But as any trader will tell you, a stop-loss can quickly become a target that simply gets triggered before the bigger trend resumes. The last thing you need if you are committed to a project is to exit just before the opportunity can be made real.
A plan, like a tree, must have branches – if it is to bear fruit. A plan with a single aim is apt to prove a barren pole. – Basil Liddell Hart, 1954
Perhaps the point then is that it is more important to get the big picture right – the big trend that will ultimately underpin the market dynamics that can support success. It’s like the difference between losing a battle and a war.
The genius of Winston Churchill was his ability to filter the chaos through the big picture, so that decisions were optimised for the very uncertain circumstances. “There must be that all-embracing view which presents the beginning and the end, the whole and each part, as one instantaneous impression retentively and untiringly held in the mind.” He had a framework within which to respond to challenges and take advantage of opportunities as they arose. Individual failures are simply events that help to shape the direction taken to achieve the ultimate objective.
David Einhorn likens the challenge to playing poker – where there are things that you know with certainty (your cards, everyone’s wagers), things you can surmise (that fellow’s playing style) and things that you don’t know except at the limits (the number of cards still to be played). There’s a spectrum of uncertainty along which calls are made. The challenge is to identify the key calls, those decisions that will make the biggest difference to the outcome – and tilt the odds in your favour. Again, it’s his ability to assess the context and then the circumstances within that determine the appropriate choices. Failing fast may mean folding early on a hand, but in doing so you’re more likely to have the resources to bet big when the opportunity arises.
So perhaps failing fast is a really a short-term concept within a long term context. And just as “a plan doesn’t survive first contact”, it’s understanding the potential points of failure and being aware of the contingent paths that will optimise chances of success. If failing fast is a skill, it is referring to the ability to dynamically choose the path not the destination. The objective remains to scale the hill, just that it may require pivoting along a few paths to get there. Even Sysiphus can have his good days when it’s as much about the journey as the destination.
Wouldn’t it be grand if our x-ray vision enabled us to separate the liars from the trustworthy? Being able to tell whether the used car salesman is really selling this car cheap or whether the shop assistant did inadvertently add that extra item on your bill. Or at work, being able to trust a colleague who seems charming enough but may just be screwing whatever they can out of you?
Lying is a deceptively complex concept. As we all create our own realities on the fly, the idea that my reality is not the same as yours is built into the system. We can disagree about what just happened and be entirely consistent with our own internal radar. This lends itself to gradations of truth.
The idea of a white lie plays on the need to protect someone from something. We lie in their best interests. Following this logic, the lies that we worry about are those that are designed to favour someone else over us.
Perhaps this opaqueness is why ‘thou shalt not lie’ did not make the top 10 commandments.
Lying is not a natural act. The sociopaths amongst us may be better at it, but it remains a challenge even for the most self-deceptive. Firstly, you must rearrange the facts to fit with the evidence. This is not always easy hence the saying that a liar will get caught in their own web.
Secondly, there is an element of guilt. We’re social creatures who like to roll with the herd. Our emotional response to breaking with the rules is to feel bad. Like all emotions, guilt is likely to motivate the liar to behave in certain ways to be consistent with the way they feel. Perhaps they will compensate for their actions, by acting in some other altruistic way.
So how can you tell if someone is lying? Here are some techniques from the professionals.
1) Pose a credibility question – this was a favourite tactic of a friend who was a stock analyst. He would start interviews with pretty straight-forward questions to build trust and relax his quarry. Then as he began to ratchet up the complexity of the questions, he would slip one in where he knew the answer – and that the interviewee would be expected to know too. A wrong answer here is a strong tell that things are not as they seem.
2) Deny, deflect, delay – we don’t like lying as a rule. It requires effort even for those that have limited emotional empathy, as they need to construct an alternate reality that still fits with the evidence. For this reason, the preferred first course of action is to try to not answer the question. So a politician may suggest that this question needs to be considered by some committee process, or they may suggest that is something that they could not have knowledge of. If someone is being evasive with their responses, the chances are that they are avoiding the truth.
3) Body language – while our words may say one thing, our movements can often say another. We often unconsciously register if someone’s body language is out of sync with their words. In fact, it’s been said that over 90% of communication is non-verbal. People who are lying will often try to create distance between themselves and the question – physically pushing back to create extra space. They may also close themselves – folding arms, legs or turning to one side – rather than meeting your gaze or opening their posture. This is not an exact science – some of us are nervous around people at the best of times. So it is relative, which is why detectives will try to gather a baseline behavior before applying the polygraph test.
We’ve been migrating to the Borg since computers were invented — integrating our lives ever more deeply into our machines. It’s not a trend that can be stopped. Instead, as Kevin Kelly suggests, we have to ‘civilize’ it.
The immediate problem is one of alignment — the business models driving many of our machines are misaligned with our collective interests. The issue only becomes more pressing as we begin adopting AI-driven personal assistants. The more we rely on machines, the more certain we need to be that they are acting in our interests. With platform coops, we aim to align interests to foster that certainty.
Exhibit A — asymmetric data-tracking
The Migration began inside corporates. We started gluing resource-planning systems together in the 1970’s, and have long since moved to patching the entire enterprise into a coherent whole. Over the last decade, integration has begun to jump the corporate fence as it has pushed up the supply chain. But, while the ubiquity of the chip may promise interactive access to each of us, integrating with customers has proved elusive.
This is why Customer Relationship Management systems still offer about the same value as a library card — they are more archives than platforms to exchange value. Most corporates remain once removed from their customers.
The response to this problem has been to develop data-tracking. This is an attempt to better understand us by collecting our data exhaust and following our movements through the world, both virtual and real.
Tracking by corporates is asymmetric. Data is collected in the shadows, under conditions that are at best tacitly agreed to in never-read terms-of-use-tomes. Corporations know this, which why they hide their activities rather than being transparent.
In short, tracking creates a surveillance economy, not a sharing economy.
Transparency leads to choice
The crazy thing is that we humans are keen on sharing. We want customised interactions with business and government. We hate spam and poorly informed conversations. And we understand that a corporation must know who we are to give us what we want. The opposite is that we remain a statistic, a number that gravitates to the lowest common denominator as scale increases.
So there is another way corporates could respond to the problem. They could just ask — chances are we will be willing to share. It’s about being transparent. If it’s clear what you’d like us to share, then we can agree to it, even if it is tacitly.
This is the type of approach that the proponents of Vendor Relationship Management espouse. “VRM tools provide customers with both independence from vendors, and better ways of engaging with vendors.”
Logically then, those that use tracking are worried that we won’t agree to share. If they hide their activities, they’ll get away with them. There is something fundamentally wrong with this proposition.
This is one of the reasons why platform coops are so important. Where the customers are the owners there is a self-regulating system to protect the interests of users. As our technologies are working for us, we can be confident that they are not leaking data we may not want to share. Conversely, we are motivated to develop tools that make sharing simpler and more effective — as that is what our customers want.
Our view is that the tipping point for shifting from surveillance to sharing will be relatively low. All it takes is a successful alternative to demonstrate its value and the tracking approach quickly becomes uneconomic for any type of enterprise.
So if we are to cross the final frontier, let’s do it on our terms. That way we can be confident that when we are rubbing virtual shoulders with a personalised corporate avatar, they aren’t fleecing our pockets too.
So I have my personal butler that comes when I pull on the bell-hop. He fetches me music, orders in dinner, and even claims to automatically restock the pantry. But what happens below stairs is a little bit of a mystery to me. I know that he scurries away to his closet and waits patiently for my next call, but how does one keep oneself busy below decks?
Turns out that our personal bot builders are thinking that there are a bunch of things to keep the butler busy. And herein is a problem for bot-builders – one that they’ve responded to by thinking that they can do more that simply sell us smart machines. They’re building the ‘operating systems’ for our daily existence. And if we are to become reliant on these systems then they can do a whole lot more to monetize our needs and wants. For example, how about clipping every transaction that the bots intermediate? You may not even have to pay for your shiny new fridge, if the fridge can take over the task of buying your daily provisions.
We can see the opportunity in this – a bot for every home, mixing our drinks and picking up the dog poo. But this business model poses one of the biggest challenges with personal bots – who are they really working for?
In the olden days, my butler was working for me. For a modest salary, he lived under my stairs, ate my food and shined my silverware. It would have been unusual indeed if he took a margin on the food that he purchased for my family, even if there was always the chance of a little slippage. The point being that the cost of the butler was effectively a fixed fee that was paid by me – this served to align our interests.
But what are the motivations of a bot that delivers profits to a third party based on the transactions that it can intermediate? It sounds suspiciously like they are motivated to sell me more and in ways that generate the highest profits to someone else.
We are being sold a fairy tale that a few companies can build the operating systems for our lives. This is not the way that evolution works. It thrives on difference and competition. We would be wise to encourage this – to have the bot-builders compete away their ‘clip-the-ticket’ business models – and before we all become dependent on our digital butlers.
In a conversation with a CEO of one of Australia’s largest health insurer’s, we were talking about the demographic problem that our society faces as upwards of 400 people a day are turning 75. The demand for aged care services is outstripping supply. As a mutual, he posited the question as to where I would prefer to have my parents looked after – in a mutually owned aged care home or for-profit one. It’s the same question about alignment of interests. I joked that they could get a mutually-owned fleet of personal bots. Perhaps it is not such a silly idea…