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!