AI searches for human intelligence , to beat

 With all the hype about AI taking over humans and humans worried about their precious skills/role in this world risk getting diminished, let us take a moment to see the examples of human intelligence . And hence the bar that AI needs to cross.

1. Most of the things humans do day-in and day-out, are done because others do it, not because of their own will
2. Humans fall for the same scams that their friends and others have fallen for, in the past.
3. The biggest movie hits are super-hero sequels. Everyone knows super-heroes don't exist.
4. The biggest sports events are sports leagues which everyone know are fake teams put together with money and have no other loyalty.
5. Millions buy lottery tickets whose chances of winning are lower than getting struck by lightning.
6. Most go to lawyers instead of settling.
7. Most buy things they don't need, with money they don't have, to impress people they don't know.
8. Most take advice from people who themselves are figuring it out, as they go along.
9. Humans make paper from trees and then fight for it, their whole lives.
10. People subscribe to IPOs knowing fully well that many rounds have already gone by, to take out all the juice.
11. Humans sometimes are like cacti eating camels that take pleasure in hurting themselves.
12. Humans fight for land on which they are short term visitors.
13. The same humans kill each other to move artificial/invisible lines on the land , by few meters.
14. They are such creatures of habit that even a kid can recce them in a matter of hours.
15. Finally the AI hasn't come from outer space. It is created by humans.

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.

The Logic Logjam

 "Logic is invented by humans and may be ignored by the universe. - Will Durant"

Ancient cultures of India, china and greece have developed and documented formal logics. The laws of deduction, inference etc are fundamental to these. Observing the happenings, analysing, debating, rationalising, linking behaviors and root causes to results are all part of our human learning.

In spite of centuries of such learning, it seems inadequate and the universe surprises us constantly. Give enough time and humans contradict/trash their own learnings.

Have we missed some variables? Do we need to observe and analyse more? Or as Will Durant said, the universe does not follow any logic?

If the world cannot be explained, how can its micro components be considered understood?

Is the endeavor of logic, self perpetuating and will forever remain inadequate by design?

On the other hand, should humans submit themselves to the vagaries of universe, ' go with the flow' and stop figuring it out to the extent possible, to make their lives a little better?

Surrogate Learning

 Reading a biography is an easy way to learn from someone else's life. Granted the situations may not repeat but sometimes they do. Are there other ways of this surrogate learning?

In business and personal contexts, learning rules is easy. Most of the times they are straight forward, binary and crisp. Learning how to 'behave' in a context/situation is difficult as the number of active parameters is too high/unique. It is learning this human judgement that can help one avoid risks/errors and get better results. Before learning the behaviors, understanding the context is absolutely critical. No two contexts are exactly same and the same individual may make a different decision/choose a different action if one/more parameters change. Multiple actions/decisions in multiple contexts may help you distill/abstract the 'rules' that can help significantly.

More often than not, the contexts are not documented clearly /succintly. They are hidden in documents/numbers/conversations and actions. Reverse engineering the context from these is not easy but neither is extracting gold nuggets from mines.

Understanding the sources of context and actions is the first step. Choosing the contexts to learn from is important as not all situations have useful learnings.

Distilling active parameters, elimination of individual biases, abstract the contexts for better re-usability, explainability of learning are all the key next steps.

Context-Action-Learning models are not easy to build but its something that comes to us innately when we learn from others, from our childhood by observing/imitating/repeating. Bringing this to scale in an automated/structured framework is where the benefits of surrogate learning lie.

Hiding in Plain Sight

 Any espionage thriller will tell you the best place to hide is where everyone can see you. Solutions to many pressing problems are also similar.

Every life/industry has long running open challenges or problems that need to be solved where the solution remains elusive. In many cases, the solution is hiding in plain sight. How to find it?

1. What is the problem? Is it the problem or symptom?
2. Is it material enough? Is it worth solving? will the impact be significant?
3. Kill ALL assumptions and conventions. Many a time, they come in the way. In the espionage example, stop searching for someone looking odd. The one trying to hide, also knows this.
4. Remove all noise ( naysayers, dissuaders, bystanders...)
5. Just as the problem took long time to develop/survive, the solution may also need time. Pain killer suppresses pain instantly but reducing weight to relieve stressed joints , takes time.
6. Can the strength of the problem be leveraged for the solution? Not different from deflecting an oncoming weapon to the enemy.
7. Can the problem be made irrelevant ( This is different from finding a solution to the problem ) ? Is there a path that does not include this problem at all?
8. Do not hesitate to try the solution(s) . " Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth. - Arthur Conan Doyle "
9. Trying every solution helps you define the problem with more clarity and takes you closer to the solution.
10. Why did we not think of this solution that has been hiding in plain sight?

Long on long, Short on short

 Typically humans tend to be positive on the long term and negative on the short term. Is this just my opinion? let's see.

1. Stock markets have a typical upward drift over the long term. Given the underlying sentiment, it is safe to assume humans are positive on the long term.
2. Humans respond to negative news ( short term) more and faster. Negative items have higher views/ engagement/curiosity. Which newspaper/news channel gets more eyeballs saying everything is good?
3. A common perception is that we have more control over the immediate/short term than long term ( Is this perception the truth, is another matter!). A long term weight reduction plan gets lesser attention than an instant gratification. More effort/money is spent globally on cures than prevention. Probably because we don't believe something bad may happen in the long term. Armageddon predictions by the cults are considered funny at best and ignored.
4. Our own attention spans tend to focus more on the short term . A game that gets done in an hour or less has more viewers than a game that runs for days and weeks.

It is probably this view that is continually shortening the cycles of consumption, economy, entertainment, pursuits that the true tectonic human endeavors are ignored.

The 3 truths

 Be it Pulitzer winning journalism, the judiciary,science, philosophy : all of them pursue truth. The problem with the truth is there are many versions of it. Understanding these versions help us understand reality and solve problems better. How?

Let us take a few examples to appreciate these better. Few years ago, world believed that someone who is good at many things will survive and grow. Multi taskers, Conglomerates, Super Apps, Jacks of all, Generalist MBAs... Firms that provided products across market segments, value/price spectrum were valued highly. ( General Electric, Samsung...).It was the reality and truth for some time. Over a period, those were found to be lacking depth/moat and were pummeled by specialists. ( Apple, Tesla, Super specialists with deep skills and expertise in a specific area, Uber, Airbnb...). Suddenly this became the truth that everyone started respecting. Hypes that go through cycles usually have an agenda owner/motive owner behind ( 'Cui Bono'). A truth that changes over time based on what is real at a specific point of time. Powerful people create a 'truth' that they want everyone to believe in. It is called truth based on a reference /authority/agenda or motive owner and is gone later when the reference/authority/agenda owner vanishes.

During multiple booms, markets, investors, governments and publics were seeing value where none existed. Dot Com boom until it got bust was glorified by everyone. Easy money based valuations in last few years made nations yearn for their own unicorns even if they were illusory. German government was praising Wirecard as a national icon until it was'nt. They were seeing something that did not exist. People who did not understand 'tech valuations' were branded luddites. This 'truth' has no basis and existence. It is an illusion.

The eternal truth is different from both the above. It neither depends on reference nor is illusory. It does not change with anything. Getting to this is not easy or straightforward. Before you get to this eternal truth, first you need to look at the current realities and see if they belong to any of the above and rule them out. Process of elimination gets you closer to the eternal truth. The clarity alone is worth the effort!