Creativity Killers

 What are the 2 absolutely critical ingredients for creativity to succeed? How well does the world provide them?

As Warren Buffet famously said, you cannot get a child in one month by impregnating 10 women simultaneously. Similarly you cannot force creativity in to an assembly line process. While Hollywood discovered that super hero sequels are the greatest bankable story lines for a blockbuster, not every sequel is a hit and neither is every remake. Irrespective of how much the world wants it, creativity cannot come against a tight timeline and a mass production scale.

Two critical ingredients are Patience and Scarcity. All great ideas take time to take shape and evolve into something impactful. They also need to be sufficiently scarce that their value is deeply appreciated. The Italian masterpieces that the world values so much are unique and are not mass produced.

However once the creativity takes shape into the commercial world, there is an incredible urge to do it faster and bigger/more. These tend to kill the very aspects that make creativity itself in the first place. A masterpiece becomes a commodity once its mass produced and the quality suffers as the process is fast tracked. In a world where luxury product makers are listed on stock exchanges that need quarterly earnings growth, there is no time for ingenuity. Forced growth forces compromises that eventually show up.

Truly creative enterprises struggle with this balancing everyday. One sees balance of power alternating between 'creatives' and 'spread-sheeters'. Some bring 'special editions' to latch on to the scarcity aspect. Some take leaps of faith on new ideas that eventually end up being useful in ways different from originally imagined. Some try to resist the temptation to 'templatize' everything while some cannot.

Those who get it right, find the rewards compounding for years and decades!

What fails AI ?

 The entire world talks about the hallucinations of AI ( incorrect answers), opacity of its workings, threat of job losses. But have you pondered what fails AI? what sabotages it and ensures its failure? When the models and tech come out of labs to the real world , what awaits them?

A very small percentage of AI projects get completed and deliver value. What comes in the way?

1. Big 'D' : For a ChatGPT, the billions of parameters get fed with mountains of data from a number of open source/ commercial data. For a business model, the data resides in multiple locations owned by various stakeholders - commercial / regulatory/users/customers : almost none offering the data easily. The sources are difficult to access , data is of varying quality and trustworthiness. Connections are unreliable . Compliance /Geopolitical /Privacy concerns can shut off those data pipes without notice.
2. Survivorship: With all the media talking up job losses, it is natural for people to get defensive. Exceptions and individual judgements are over-emphasized, early mistakes are magnified and model adoptions happen with varying levels of reservations.
3. Trust : Data legacy and past experiences do not always contribute to users trusting the base data much less the decisions /predictions /recommendations.
4. The invisible demons: Efforts to establish data pipelines, enhance /standardize data quality are underestimated and under staffed. For businesses looking to see quick results, these pose some real big obstacles.
5. Dynamism of business : Market Place is not static. Customers/competition is changing continuously , Newer nuances come in that do not have history. Newer processes/ process attributes and market dynamics may mean the project contours may become drastically different.
6. Costs of a right/wrong decision: Not every process needs to be as precise as a Japanese bullet train. Some times the manual processes / semi-automatic/automated processes are efficient enough and AI may not add incremental value to the extra cost.

While every project has its own challenges and surprises, AI projects tend to encounter unique situations . Planning and managing them could make a difference between real impact and a statistic.

The 'Moat' myth

 Are moats sustainable anymore? Are they just glorified differentiators that are transient? Can they help you identify a long term winner?

Since Warren Buffet talked about moats for corporates, everyone in the investing world latched on to this buzzword to sell investments. Moats are the ditches around castles in olden days to protect them in case of wars and in a corporate context, they are the big advantage a corporate has over its competitors in the market place to keep winning customers at high margins.

Over the last 10 years, companies highlighted business models, scalability, Product design & pipelines, R&D, Intellectual property, Customer orientation, superior cost structures, sovereign support, technology etc as their 'moats' that help them maintain unfair growth and profitability.

Things seem to have changed. Some of them:

1. Geopolitical concerns mean R&D needs to be localized and is no longer as 'hidden' as before.
2. Talent is lot more globally mobile , thanks to remote work and tech
3. Governments no longer allow businesses to collect/monopolize data
4. Algorithms can no longer be opaque, thanks to ethical/political concerns
5. Underlying business components are now available to anyone , be it funding, technology on cloud, open sourced programs and models, skills, work places and even supply chains
6. Business boom/bust cycles are much shorter making accumulation of anything a double edged sword
7. Thanks to explosion of financial markets and technology, everything is funded ,sometimes on opposite sides.
8. Hence,replicating a successful idea now is much faster, easier and cheaper.

What now is the 'Moat' that is defensible , at least in the medium term ?

Is there anything that qualifies to be a 'Moat' any more?

(Probably) the most precious skill

 What gets paid the most? Which skill should one acquire ? What skill may not become irrelevant due to AI or something else in future? As every profession worries about getting automated or diminished relevance or impact , this question is worth probing.

Why does a CEO or a Captain of a sports team or a leader get paid multiple times more than a player or an employee? Do they put in X times more hours than the player/worker? Do they work X times harder? ( they have the same number of hours in a day/week as others). Also for something to be shielded from automation, it cannot be predictable or repeatable. It needs to be vague enough to not fit into rules but valuable enough to make significant impact ( to justify those sums).

Leaders have capital/resources/players at their disposal and multiple people may have the same options. What perhaps distinguishes and makes impact, is judgement. In close encounter games, a small steer/nudge/decision by the leader changes the game for everyone and determines the end result. If that judgement works ( at least most of the time), the money paid is well worth it. On large decisions, the right judgement ( which probably has taken much less time or effort ) has an outsized impact. A slightly better steer has a huge differential gain. The scale of the decision/resources amplifies the impact of the judgement.

Once the basics are commoditized and standardized, the difference between the winner and runner-up is usually micro-seconds.

It is debatable whether this can be called a skill. Irrespective, can this be automated /eaten away by AI? The contexts are different each time. Same decision taken in different contexts has vastly different results. The underlying parameters are many and are not same every time. Many a time, some of the defining parameters of the context are unknown or poorly known. Safe to assume that it is not easy for an algorithm to learn this.

As one moves from measuring efforts to results, it is not the hours/days that are spent tactically but the seconds when the right judgement is made, that matters. And that is a skill/attribute that is worth learning any day!

Value in sweating the 'hard' stuff

 Do we really need MIT /Stanford / IIT engineers to figure out drivers scheduling/ delivery agent routing /hotel booking? Is that the best allocation of skill capital?

(Whether coding can be considered engineering is a different matter altogether)

The world is full of complex hard problems that urgently need solutions to improve the quality of human life. Getting the right skill resource to work on the right problem is a critical aspect of human endeavor to get better.

Even for a business, tinkering at the edges isn't really where the best minds should work. From a customer impact perspective, this tinkering doesn't add up to much. Trivializing customer /industry pains , means you are 'ignoring' them at your own cost.

Complex hard problems need

• getting back to the first principles,
• getting to the basics,
• going back to the drawing board and
• not taking any assumptions for granted.

Such efforts are surely worthy as they result in quantum impacts to customers lives which in turn elongate a corporate's lifespan.

While an Uber or an instant delivery app certainly makes life a bit more convenient, a non- polluting, smart, continuously learning car has a much bigger impact in an industry and the world. While one may love or hate Tesla, the sheer perseverance to 'engineer' a solution to a complex problem ,changes the industry/world massively ( and gets rewarded accordingly). It also is not so easy to imitate, as many are realising, giving a long term 'moat' to the enterprise.

It is not easy, quick or tactical. And that 's precisely why it matters!

A picture is NOT worth thousand words

  Thanks to tools such as Midjourney and Stable diffusion, one can convert text to images without breaking sweat. But thats not the big picture!

Strengths of these models are also their weaknesses. Let me explain.

The models look at past visualisations for the words and replicate to the closest possible. However the written word is lot more powerful.

Any description or story line can be visualised in 100s of ways which is only limited by the reader's visualisation and the text articulation. More detailed the articulation, more specific is the visualisation. Conversely less detailed the articulation, more visualisation possibilities exist. 

Have you ever felt that a song when picturised or played on stage did not do justice to the words? It happens when your visualisation does not match with the director's. Neither is right or wrong but it only shows the number of visualisations possible against an articulation.

A specific visualisation is in effect limiting the possibilities for the articulation. Same story can be picturised in multiple ways based on perspectives. 

Word hence is always more powerful than the image as it allows us to excite neurons in our brains in multiple ways, often differently at different points of time. 

May be it is time we re-look at reading and writing not as skills in decline but powerhouses that offer more possibilities than what a model can deliver!

Adopting AI

 While the hype and market valuations sky rocketed about AI since ChatGPT launch, mass adoption especially with enterprises need AI to cross some significant thresholds:

1. Explainable: Every decision/recommendation needs to be 'explainable' in terms of how it is arrived at, what is the basis, how robust is the deductive logic and also how easy is it for regulators to understand and be comfortable with the rationale .
2. Consistent and Repeatable: Many business processes expect and demand consistency and repeatability. Same question or transaction needs the same answer or decision every time. Consistent client experience , regulatory compliance , profitability and risk management cannot be ensured otherwise.
3. Transparent: Wide spread adoption also demands simplicity and transparency. Decision/recommendation/output cannot hide behind complex mathematics/models that cannot be interpreted easily.
4. Ethical: Guard rails to ensure the AI output meets the standards of Common Good, Socially acceptable norms in the culture and legal righteousness. Historical biases due to the nature of data, discrimination, sensitivities to user age etc - how are these addressed?
5. Secure : How secure and private is the input data? How is the balance achieved between global learnings and local data privacy ? How tamper proof are the results?
6. Better than human : As governments and economies push job creation, can AI be at least as good as human, if not better? In a number of situations, humans are better than machines due to a variety of factors.

It is CI, not even remotely AI

 Before you run to buy the next 'AI' stock/ learn the next new 'artificial' skill, here is an unfiltered view on the current hype wave:

1. Crowd Information (CI) , not Artificial Intelligence (AI): The algorithm does not 'know' anything about what it produces and gives you. It cannot find or create something on its own but is running multiple mathematical calculations on the past information across internet and other sources to 'guess' what should be each word of the answer you are looking for. Even a self driven car is repeating what 'should' be the action, based on millions of actions it observed. It has no mind or discretion of its own. So if anyone is imagining an artificially intelligent monster behind the screen, you are more likely to find them in a Hollywood horror flick.
2. Having found that paying 30% to a toll booth operator like Apple or Android is not good for their own business or ideas, many have shifted to open source and the models that do the mathematical churns are likely to be not 'owned' by anyone. As a corollary, the next best model /algorithm may be free in the open source world. So AI printing bundles of money is currently a dream.
3. Any job loss due to AI ( or CI more accurately) is more due to the predictable /biased behavior of humans rather than a sinister plot. We are creatures of habits and it is not very difficult for someone to imitate us at lower cost. Same for the biases and consequences. If we have taken centuries to slowly reduce them, the algorithms will take a bit more time to learn from our behavioral changes.
4. Intelligence is a complex idea. There is no uniform definition of what is intelligent. If it is about avoiding errors/mistakes, doing the right thing, finding the most optimal solution etc , all of these are continuously evolving. A masterstroke may become a costly mistake in retrospect and there is always a more optimal solution if we imagine better.
5. Artificial: How is anything artificial if it has all been 'learnt' from human actions and is programmed /managed/modified/enhanced by humans all the time?

Like all buzzwords, AI is more a clickbait than what it says. In simple words, as our physical education/yoga teacher says, it is "Observe and repeat"!

Tail win(d)s!

 Both iphone and Amazon Prime which drive bulk of the revenue and success of the two companies are among the 100s of products tried. While its common for venture capital to find one of 10 investments succeed, even in public markets, the index is carried by less than 10 companies and many others are either status quo or laggards. Ignore tail at your own peril!

Consider a business that is doing fine in terms of revenue, profits etc. Its world is not static. New competition emerges every day to take that margin, beat prices down and commoditise the business. Customers move on to something better or new, regulations change, technology alters the business landscape and so on... It is critical for any business to have a tail that may come to the rescue once the head is down. When iphone or Amazon prime were launched, they were not expected to be the blockbusters they are today. The number of times Nvidia bet its life on something new is quite a story. It did not work always but Nvidia did not stop or cut the tail. Many of the stalwarts of the index no longer exist. This is after a great amount of screening and consistent performance that was mandatory for a company to get into the index.

'Jack of all and Master of none' is the saying. What you are master of , may no longer be relevant or in demand forever. Human ingenuity keeps striving to find the unknown which in many cases is opposite of what you know or believe. Inflexibility or hubris that cause one to not be open to the new, may cause the fall that happened to many index constituent companies.

It is human nature to ignore the tail as its seen as an appendage and is not immediately useful. When you fall into a ditch, that may be what pulls you up!

Human Bots

 Algorithms are biased, predictable,dumb, incorrect, unethical and much more. Now just replace the word 'Algorithms' with 'Humans'.

Thanks to analytics ,we now know that most of us order a few food items ( less than 10) in each location . While Baskin Robins has 30 flavors ( one for each day of the month), best selling are not more than 5. Bulk of the hit movies have the same formula for decades but we still like them again and again ( be it the comic super heroes, revenge dramas or situational comedies). Every grocery subscription on Amazon reinforces the fact that inspite of 100s of brands available, we stick to the same month after month. Watch any tv debate and you will find one or two panelists who spew the same stuff that popular ethics deplore. News/TV/Social media contain the same inaccuracies or lies that everyone criticises algorithms about.

Is it any surprising that algorithms which learnt from the same human behavior are biased, predictable,dumb, incorrect, unethical and much more? Why do we hold them to a different standard from their creators' ?

Ask 10 people the capital of a country and chances are most would be wrong. Unless someone corrects the humans of their incorrect answers, they carry the same wrong information to their graves. An algorithm once corrected ( which is entirely possible through feedback loops), never repeats the mistake. We cannot guarantee that humans don't repeat their mistakes. If humans become infallible by learning, we wouldn't have so many traffic signs!!!

When automobile industry started with assembly lines, it had lots of people doing repetitive tasks in a specific order and to a specification. Today most of the lines are replaced by robots with a very small number of humans addressing exceptions/errors. Cars made by these robots are running for tens of thousands of kilometers/miles and world seems to be fine with them. While recalls are costly, they are much fewer today than earlier.

Predictable, sometimes quirky human behavior lends itself so well to feed the algorithms. We are more predictable /biased than we think and we are more times wrong than we care to admit. If anything, algorithms repeat the same behavior tirelessly.

Benefits of any technology are almost always under-estimated or over-estimated. Before judging them, it might be worthwhile looking into the mirror.