Apr 29, 2023 | Change, Innovation
30 years ago tomorrow, April 30, 1993, the public internet was born with the announcement by the European Organisation for Nuclear Research that they would publicly release the HTTP protocols that would change the world. These protocols had been created by Tim (now Sir Tim) Berners-Lee for use by academic and defence facilities and had been very tightly held. On April 30, 1993 they were posted on what would become the world’s first website and were to be freely available to all.
10 years ago, I posted a happy 20th message.
Leading up to that momentous release of the HTTP protocols, providing the initial foundation for today’s internet, the US department of defence had created the ARPANET (Advanced Research projects Agency Network) in 1969. The first email message being sent by Ray Tomlinson who first used the @ symbol to separate the recipient’s name from the network address to himself in 1971. By 1983 there was general agreement on the standards for communication on the internet, the TCP/IP (Transmission Control Protocol/Internet protocol), and in 1985 the first domain, Symbolics.com was registered, which remains live today.
Once publicly released, the standardised protocols saw a mobilisation of innovative resources from around the world, resulting in rapid development of uses and tools.
Mosaic, the first popular web browser was launched in late 1993, and later was renamed Netscape navigator, and Yahoo launched in 1994. Microsoft launched their competitive search tool Explorer in 1995, later incorporating it free into Windows, leading to the move by the Clinton government to take action under the antitrust laws in 1998, resulting in an order to break up Microsoft. This order was later lost on appeal, significantly due to the evolving dominance of Google as the preferred search engine. Amazon launched in 1995, Google in 1998 and amongst the wave of tech IPO’s in 1999 was Napster, the first peer to peer file sharing service.
From the launch of Wikipedia in 2001, we again had a wave of launches, most of which failed, but a few became the unicorns that changed our lives, Facebook 2004, YouTube 2005, iPhone 2007, Instagram 2010, and so it continues.
The most recent inflection is obviously the explosion of AI tools since the release of ChatGPT in November 2022.
If you extrapolated from this birthday out to the next milestone, the 40th, the only thing we can say for sure is that you would be wildly, massively wrong. That happens every time such an inflection point is reached. Extrapolation is useless, instead we need to experiment and innovate, a continuous process that will take us in completely unpredictable directions.
I hope I am around to see it.
Apr 17, 2023 | Change, Innovation, Strategy
Over the years I have helped a number of start-ups. Almost all have been single or a few people who have a drive to start something they own, where they can call the shots, and be away from the dead hand of corporate bureaucracy. Sometimes this has been a formal assignment, more often, the result of a series of casual conversation in cafes, networking meetings, and at BBQ’s.
Across these conversations, there have been some consistent themes,
They focus too much on the little things that do not matter much in the long run.
Logo, company name, design of the proposed website, details that do not make or break a new business. At the early stages, these things can be easily changed, modified, and often are dumped.
What this does is take the attention away from what really does matter. Clearly defining the product and/or service to be provided, who is the most likely ‘ideal’ customer, why they should buy from you rather than elsewhere, and how they communicate with them about the value they can deliver without wasting resources. These are the things that matter. Their common characteristic is that they are qualitative, hard to measure, and they evolve.
Evolution happens on auto pilot, make it positive.
Things change, often they change while you are not looking, and only become evident with the benefit of hindsight, by which time it is sometimes too late to do much. The other side of the coin is that evolution also applies to the good things. The task of a new company is to set the guiderails so that the good stuff outweighs the bad. Alignment of all personnel and outside stakeholders is vital in this process, as the pressures will be coming from all sides. And like a child learning to walk, you need to have some of those guiderails in place, or you will wander off in random directions. I call it having a robust, deeply considered strategy.
Imposter syndrome always plays a role.
Unless you are a sociopath, imposter syndrome will grab you from time to time, you will feel out of your depth, wondering why everyone is looking to you for direction and confirmation. It will feel like that first time on a big public stage, dread about what is to come. When you look back, assuming you have done the preparation, you will recognise it for what it really is, a test, and a great learning opportunity.
Spreadsheets are liars.
Most businesses start with some sort of plan, most often articulated via a business plan template and a few spreadsheets. If you are looking for outside finance, these will be mandatory. However, I have never seen a spreadsheet or written plan that accurately reflects what actually happens. Most are nice, comfortable extrapolations of continuous growth along a predictable path. The growth of every successful business looks like a game of snakes and ladders. 3 steps forward, and whoopsie, 2 backwards. The trick is to ensure the steps upward and forward outweigh the falls. Sometimes this simply does not happen, and the snake hole swallows those who fall into it. Spreadsheets never allow for the ‘snake-holes’
Internal Vs External.
Most start-up failure comes from two sources. Firstly, from the lack of cash management. To my mind, there is no greater sins that not being proactive with cash, a simple set of disciplines often ignored. The second is because they have neglected the management of their customers by looking inwards, managing the inevitable personal and process friction that occurs, rather than looking at how they can add value to their customers. Customers do not care about your internal challenges, they are paying you to release them from theirs.
People are your greatest asset, and liability.
A business without people is just a scrawl on a piece of paper. A ‘micro-business’ which is what almost every business is at birth, can be strangled by one poor choice. Equally, that one choice can be the making of you. In the early days, when everyone is acting in all sorts of roles, you need people who are self-reliant, resilient, and happy to ‘muck in’. They are very hard to find, and even harder to keep when you do find them. Equally, when you think you have found the one, only to realise they are not as advertised, which is what they were doing during the interview, remove them quickly. A wrong employee at an early stage can become toxic very quickly. Sometimes that person is great at what they do, are seen by others to be vital, but they are a pain in the arse for some reason. Experience tells me that the benefits of what they are good at are usually outweighed by the hidden costs of them not being aligned with the rest of the team, and its objectives.
Being seduced by opportunity.
That old cliché of working in the business instead of on the business is almost always true in the early days. You will be swamped from all sides by problems as well as opportunities, both of which will radically dilute, if you allow it to, that characteristic of successful start-ups: focus. Plan for what comes next, focus your very limited resources on the key drivers of that outcome, and eliminate everything else. This is never easy, but is absolutely necessary.
None of this is easy, if it was, everybody would be doing it.
The failure rate of start-ups from the corner coffee shop to high-tech gizmos is very high.
Finding the right sort of outside ‘reality check’ advice and input that delivers true value is perhaps the eighth challenge, which so many get wrong, but which can change the outcome dramatically.
Header credit: Arrived via my new AI mate Dall-E
Apr 5, 2023 | Change, Culture, Innovation
Never before has the need for creativity been more critical.
Never before have set about crushing creativity before it has a chance to bloom more than we do now.
My nephew is dyslectic, always had trouble at school, with teachers, sitting still, and anything that required him to read and write. In a parent-teacher interview when he was about 12, my sister was distraught and angry to hear that her son, who had by then built a computer from bits and pieces, powered by a cobbled together solar panel on the roof, would be lucky to progress beyond being a day labourer.
He was lucky. After scraping into a regional university with a practical focus, he earned a masters degree in electrical engineering, got bored, and went back and did medicine. He is now an ophthalmic surgeon, restoring sight in the footsteps of Fred Hollows.
Had his practical talent not been recognised by an academic with a long life of non-academic experience behind him, my nephew may have continued tinkering in the garage while making his living on a production line. What a waste that would have been.
How many like him have we wasted?
How many like him will we continue to waste as we dose up the kids who cannot sit still in school, or colour between the lines, with Ritalin?
Back in 2008 an executive coach named Wayne Burkin wrote a book called ‘Wide Angle Vision: beat your competition by focussing on Fringe suppliers, Lost customers, and Rogue employees’. The title says it all.
Creativity and the resulting change does not come from those who can colour between the lines, always behave in a disciplined manner, are prepared to do as they are told at all times. It comes from the outliers, the originals, the rebels, as Steve Jobs noted, those who ‘Think Different’.
Seth Godin’s remarkable essay introducing us to the ‘Purple Cow’ resonates even more now than when it was written back in 2003. Paragraphs 5 and 6 should be reproduced and stuck on every wall of every room that ever has a student of any kind in it, and every office of anyone seeking to be a leader.
Never have we needed those who think different to have their hands on the wheel of the companies and institutions that together make up the economy, and will shape our kids futures more than we do currently.
Header cartoon courtesy of gapinvoid.com
Apr 3, 2023 | Innovation, Lean, Management
The double entry bookkeeping system we are familiar with, or should be, has been around for millennia. In the form we now know it, double entry bookkeeping was codified by Franciscan monk Luca Pacioli, a collaborator of Leonardo da Vinci in a mathematics text published in 1494.
It remained largely unchanged, just increasingly complex until the 1920’s when Alfred Sloan, the king of General Motors for 50 years developed the system of management accounts we still use, with standard product costs as a foundation.
As the ‘lean manufacturing’ movement, pioneered by Toyota, extended throughout the western world from the late 70’s onwards, the system of standard costs became increasingly problematic.
It tends to set in stone the assumptions that are built into the standard product costs, rather than using them as a basis for continuous improvement. Even worse, management KPI’s tend to be centered around functional silos that have little to do with the overall productivity of assets in delivering value to customers.
I have been subjected to ‘stalking’ variances, those that seem never to go away, but persist in defiance of management edict many times. The easiest way to get rid of them is to adjust the standard. Not very smart, but accepted practice and often the only way to achieve KPI’s in a corporate environment. It also has the effect of hiding opportunities for improvement, and ensuring reliable data is not available in real time. In parallel, we have increasingly digitised operational processes by multimillion dollar installations of MRP (Manufacturing Resource Planning) and its more expensive sibling ERP (Enterprise Resource Planning) systems. These tend to set in stone standard costs and variances down to the micro transaction level contained in work orders, which complicates and adds cost to the reporting and management processes without adding value for customers.
One of the core ideas of Lean is ‘Flow’ which is at odds with standard costing systems. Standard costing gives precedent to operational efficiency at individual stages in a process, rather than flow through a whole system, ignoring varying capacity and efficiency constraints. This results in several usually uncomfortable conflicts.
Two examples:
- Lean seeks to reduce inventory of all types, raw material, work in progress and finished goods, seeing it as a cost, tying up working capital. Traditional accounting treats inventory as an asset, and when a Lean project reduces inventory, it reduces the current assets in the balance sheet, giving a misleading perception of financial performance.
- Lean focusses on capacity utilisation and ‘Flow’ through the processes necessary to create a product. Capacity is the key operational constraint, but does not appear anywhere in the general ledger other than by inference, as a function of capital invested, the calculated value of inventory, and unit sales. Delivering capacity is only of value when that capacity is used to add value in some way, usually by producing more product from the same fixed cost base. Standard costing ignores this reality of operational management.
There are no easily GAAP (Generally Accepted Accounting Practice) conforming measures for calculating immediate capacity utilisation, and flow, and no sensible calculation of actual product costs on a short term basis that conforms to the standard cost model. A second set of measures, which use the same data base as GAAP accounting, but in different ways is necessary.
While it will take work to set up these alternative measures, once deployed they will reduce the reporting workload and error rates inherent in the highly transaction based standard cost models, delivering both utility and accuracy to operational reporting and analysis. Deployment is however not like installing an ERP system, it is a process of continuous improvement.
Setting out to implement a Lean accounting environment in the absence of collaboration and mutual understanding at the senior executive level, is akin to climbing Everest in a t-shirt. Success requires a complete change of mindset from that taught by most accounting institutions where the concentration is on financial and reporting compliance, rather than gathering and critically analysing the information that enables better management decision making and continuous improvement.
Header credit: Nick Katco from ‘The Lean Accounting CFO’
Mar 13, 2023 | Innovation, Marketing
ChatGPT has blasted into our consciousness over the last 2 months. It has created an equal measure of excitement as people see the opportunities for leveraging their capabilities, and dismay at the problems they see being created.
Both are right, but if we are to make judgements about which side of that fence we choose to sit, it makes sense to understand a little bit about how it works.
These AI tools work on letters, and groups of letters, which then make up words, and the probability of one letter following another, and then another, and then one word following another, and another.
There are about 40,000 commonly used words in English, and billions of words published. From this database computation can give you the probability of a letter following another, eg. The probability of a U following a Q is very high, the probability of a V Following an L is low. This probability logic is extended to groups of 3, 4, 5 letters, one calculation of probability at a time. The outcomes of those cascading probability calculations transforms letters into groups that make up words based on the text used to ‘train’ the software.
Many words have multiple meanings, depending on the context in which it is used, homonyms. Sometimes the spelling is different, but they sound exactly the same. We understand what is meant by the context in which the word appears. For example: if I said, ‘I am on leave’ everyone knows I am on holiday. By contrast if I said, ‘I am going to leave’, it means I am about to depart whatever event we were at. I might also leave something for you at the door.
The word ‘leave’ is spelt and pronounced exactly the same way every time, it is the context in which it is used that makes the difference.
The juxtaposition of words also makes a difference to our understanding. If you remember your primary school grammar, it is all about the position of the subject and the verb.
If I was to say: ‘I am going to leave the party‘ the subject, object, and the verb are in the correct position in English for easy understanding. If I was to say ‘the party I am going to leave‘, most would understand, but would be expecting me to say more, despite the words being identical, it is just the position that changed.
Linguists have studied these relationships for years. Their mantra is: You will understand a word by the company it keeps.
If you take this to its logical extreme, the position of every word in a body of text has an impact on the understanding of every other word, and group of words in the same body.
If the surrounding text to my sentence is about going to a friend’s place for a drink, that will lead to a probability that the ‘party’ has to do with a social event. On the other hand, if the surrounding words were about politics, the phrase ‘I am leaving the party’ takes on a completely different meaning. All these considerations are taken into account by the magic of the probability of me leaving the party when the words friends and drinks are in the surrounding copy. Should those surrounding words be government, and policy, it is more likely the party I am leaving would be a political one.
The operating system of Open AI, and others, have scraped the web for all text published, and stuck it into what amounts to a huge multidimensional spreadsheet. The machine calculates the probability of any one letter appearing after another, then any word appearing next to another based on the occurrences of those letters and words and groups of letters and words in the scraped text. It does this over and over again, spreading the web of probabilities of words and groups of words appearing together, in a particular order, wider and wider, one word at a time, across the body of copy.
This process is extraordinarily computationally intensive. It is hugely expensive to build and program machines that can do these enormous sets of calculations on this amount of text.
If you give such programs a general brief, the best it can do is return a general response. The more detailed you can make the brief, the more explicit the context, the better the machine will be able to use probability to find that combination of words that best matches your requirements, then spit out a response to you.
As a marketer, you understand that when giving a creative brief to an ad agency, the more detail you can give the creatives, the more relevant will be the creative responses. A general brief will give you lots of ordinary creative responses. By contrast, a detailed brief that clearly articulates the target market, product benefits, and the value to be derived from the products use, will generate better creative responses.
ChatGPT is no different, so for good results, give it a good brief.
What makes this so powerful for those who are expert in their domains, is that they will be able to give better briefs, and so have returned better results, which will then be the basis of their creative thinking. This offers the opportunity to improve on the best that has been done to date. For those who are not as expert, their briefs will not be as good, the context in which the machine defines probabilities will be wider, so the output more general, generic, average, and average these days increasingly simply does not cut it.
I hope that helps.
For a more detailed and technical explanation of how ChatGPT works written by an expert, go to the fifth PS at the end of this blog post published when I first stumbled across ChatGPT in December last year.
Header Credit: Dall-E. The brief was ‘ChatGPT algorithms working hard to compute copy in a surreal setting’
Feb 24, 2023 | Change, Innovation
Hemmingway observed in ‘The sun also rises’ that ‘the future comes slowly, then all at once’.
He has been proven right many times.
Since the release in November last year, ChatGPT has proven the future of AI is here, all at once.
That reality leads to the key question: so, what now?
We often look back on the spread of electrification as a template for thinking about the digitisation of our economies. It is a fair representation except for one small detail, which makes all the difference.
Electrification was a process that proceeded sequentially, piece by piece added as efficiency improved. From the beginning of the digital age, and the recognition of the reality of Moore’s Law, this has changed.
The driver of change has been compounding, each stage building on the previous, with increasing speed. While this has been seen by most as just normal improvement, the cumulative impact has been far greater.
Einstein noted that the most powerful force in the universe is compounding. Imagining the impact of compounding is really hard, makes my head hurt. To imagine it, there is still no better metaphor than the old rice on the chessboard fable.
The emperor promised someone (probably an ancient consultant) a payment in rice on a progressive scale, calculated as doubling for each of the 64 squares on a chessboard. 1, 2,4,8,16,32, and so on. It seemed like a good deal to the emperor who was clearly not mathematically minded.
By the 31st square, payment topped a billion grains of rice, enough to cover your average ancient town square. That is where the problems started as payment kept on doubling, quickly outstripping the total world production of rice.
The tipping point is somewhere around square 25, where the rice was a couple of wheelbarrows full, then seemingly suddenly, it became a vast amount.
Such has been the case with digitisation.
We have been watching its progression since Gordon Moore wrote his 1965 article predicting a doubling of the number of circuits on a single chip every 18 months. A bit like the emperor, we have watched and suddenly it seems we have reached a tipping point led by ChatGPT and its sibling DALL-E. Hot on Chats heels came ‘Bard’ from Google, although stumbling at the launch last week, and no doubt Amazon and Apple are close behind.
The difference we face to that faced by the emperor, is that had he used his abacus, he could have predicted the outcome of his agreement, as it is calculable, to a point. What happens now with the compounding of AI is not so predictable. What we do know is that it will be a disruptive force coming at us with compounding speed and power.
This power to increase the speed, accuracy, and therefore efficiency of the processes we digitise will extract a range of very high tolls. These will be the increased risk of personal data being available and almost inevitably used against us, amplification of bias, ever increasing complexity of the systems we will come to absolutely rely on but not understand how they do what they do, and a complete ‘rework’ of work. This revision of work will make the changes from the cottage industries pre industrial revolution look like minor adjustments by comparison, and will happen at lightning speed.
Of concern to me is that only a few have the scale necessary to ‘train’ these systems. Microsoft, Amazon, Google, and Apple have that scale, which will serve to entrench their dominance in the space. Theoretically governments also have the scale, but will be hobbled by concerns unshared by commercial players.
Within a decade, every current job, those that remain, will be almost unrecognisable, and there will be new jobs we cannot yet predict taking their place. What will remain is the human element of creativity, that capability that distinguishes human beings from all other species, the ability to do something completely new.
The good news is that we will still need engineers, architects, doctors, plumbers, and bricklayers, but the shape of their day will be nothing like it is today.
When digital photography took off, putting a quality camera in every pocket, most thought it was the end of photography as a profession. Not so. What became quickly obvious was that there was a clear distinction between the real, creative skills of the elite photographers, and those of the ordinary. The pareto distribution of photographic skill applied, and those that survived as professionals had more time and better tools with which to capture and express their images. This will be repeated in every job across the economy.
Unanswered is the question of how we educate our kids to thrive in a work environment we are unable to visualise.
Header credit: Dall-E. The instruction I gave Dall-E was ‘Surrealist impression of the change from cottage industry to knowledge work’ This was one of 12 generated in about 30 seconds. Look closely at the face.
