Machine Learning and AI: A Big Tech Bubble

Machine Learning and AI: A Big Tech Bubble

Geoffrey Chaucer said "All good things must come to an end ". The euphoria associated with nice things in life is normally followed by a lull. Everyone experience sapped energy levels with lack of any activity whatsoever. Overall, it is marked by clogged and cluttered opinions about self and surroundings. Could this ebb be generalized to other facets of life. Can we identify cycles of crest and trough in all phenomenons. In the recent years, Machine Learning has gained a lot of traction and has been touted as 'The Next Big Thing'. Through the course of this blog we will see some famous tech bubbles and if Machine Learning fits the bill.

A Tech Bubble is characterized by an unrealistic increase in perceived value of something. This is normally speculative in nature where companies follow herd mentality and try to board the bandwagon.  There is a collective thinking directed at the 'Utopia' where all will gain, prosper and the frenzy will be perennial. New concepts evolve to justify the actions while keeping fundamentals at the backseat. Market corrections eventually catch up as very few is materialized from the stack of forlorn promises. 

                                         As real as it gets

Let us now look at Machine Learning as a tech bubbles and draw some similarities with the previous ones.

Dot Com bubble: It started in 1995 with a surge in stock prices of a lot of internet based companies such as boo.com, Pets.com etc. Even though there was no profit shown by these companies, a lot of money was being invested in them. All realms of rationality were crossed in creating the hype around the 'coming of age' companies. This stirred an insatiable itch in general public to cash in now or never. Stock market rallied initially much to the satisfaction of investors with index such as 'NASDAQ composite index' and 'S & P 500 index' peaking just before the collapse. Eventually in 2000, market correction got the better of stock market and most companies had to file for bankruptcy or settle for depreciated valuation. The turn of events have been beautifully captured in the book 'boo hoo' by Ernst Malmsten.

Crypto Bubble: This one has caught attention of most economic noble laureates. It started in 2009 and has been the bone of contention of most monetary agencies pipping it as 'worth nothing'.   The price of a bitcoin (crytpo's favorite son) has seen an unprecedented increase in the last few years peaking around 2016-17. Ever since then it has nosedived and continues to wither away. What led to such an unimaginable surge in price ? Possibly it captured the imagination of general public as a way to increase their earnings. Word of mouth created a domino effect leading to more subscribers risking their hard earner money. The reasons for the increased price can only be explained by the dynamics of supply and demand where more people were willing to take the plunge with few exchanges to be had. In spite of  its immense popularity, several regulators discounted it with few even suggesting it to have no intrinsic value other than black marketing. This mala fide eventually led the exodus of money from it.

Machine Learning (ML): This has been touted to change the course of mankind. It will solve  complex problems, increase profits, reduce cost, enhance automation and what not. But does it really have that fire power to deliver ??? Sure it does. But it depends upon what means are used to achieve the end and  I will come to that later. How it fits the bill of being a bubble are discussed in the below points:

1. Machine Learning requires an upscale in IT infrastructure. A lot of companies have pumped in huge amount of money in it(very similar to IT and Telecom companies during Dot Com bubble)
2. Companies/VC that have invested huge amounts believe that there will be payoff at the certain point which would justify its humongous cost
3. Top MNCs and start ups have embraced ML and have hired workforce by drooling out heavy paychecks. People with few very years of experience are getting paid like anything. 
1. The issue with this is that there are very few who actually know how to put Machine Learning to any practical use
2. Skills such as ETL, reporting, warehousing that come under the umbrella of Analytics (which also includes ML ) are still more relevant than ML as the work is more defined and workforce is more seasoned
3. The workforce often lacks the necessary skill to identify use case specific to the industry. Without proper use case there is no relevance to ML
4. People talk about Deep Learning, Computer vision but still cant show how it brings any significant improvement in results obtained from simple ML/analysis/plots
4. Clients/stakeholders dont really buy the results of  an ML as they are still comfortable with excel tables, charts. Tuning alpha, beta of an algorithm is not really their cup of tea
5. As the industry matures, VC and big companies will eventually realize that ML doesnt really results in any significant improvement in the offering and that will be the start of real market correction/normalization as far the workforce is concerned (bubble will burst)