Beat your Bot: Building your moat against AI

     It seems like a lifetime has passed since artificial intelligence (AI) became the market’s biggest mover, but Open AI introduced the world to ChatGPT on November 30, 2022. While ChatGPT itself represented a low-tech variation of AI, it opened the door to AI not only as a business driver, but one that had the potential to change the way we work and live. In a post on June 30, 2023, I looked at the AI effect on businesses, arguing that it had the potential to ferment revolutionary change, but that it would also create a few big winners, a whole host of wannabes, and many losers, as its disruption worked its way through the economy. In this post, I would like to explore that disruption effect, but this time at a personal level, as we are warned that we risk being displaced by our AI counterparts. I want to focus on that question, trying to find the middle ground between irrational terror, where AI consigns us all to redundancy, and foolish denial, where we dismiss it as a fad.

1. Mechanical/Formulaic vs Intuitive/Adaptable: Well before ChatGPT broke into the public consciousness,  IBM’s Deep Blue was making a splash playing chess, and beating some of the world’s greatest chess players. Deep Blue’s strength at chess came from the fact that it had access to every chess game ever played (data) and the computing power to evaluate 200 million chess positions per second, putting even the most brilliant human chess player at a disadvantage. In contrast, AI has struggled more with automated driving, not because driving is mechanically complicated, but because there are human drivers on the surface roads, behaving in unpredictable ways. While AI is making progress on making intuitive leaps, and being adaptable, it will always struggle more on those tasks than on the purely mechanical ones.
2. Rules-based vs Principle-based: Expanding the mechanical/intuitive divide, AI will be better positioned to work smoothly in rules-based disciplines, and will be at a disadvantage in principle-based disciplines. Using valuation to illustrate my point,  accounting and legal valuations are mostly rule-based, with the rules sometimes coming from theory and practice, and sometimes from rule writers drawing arbitrary lines in the sand. AI can not only replicate those valuations, but can do so at no cost and with a much closer adherence to the rules. In contrast, financial valuations done right, are built around principles, requiring judgment calls and analytical choices on the part of appraisers, on how these principles get applied, and should be more difficult to replace with AI.
3. Biased vs Open minded: There is a third dimension on which we can look at how easy or difficult it will be for AI to replace humans and that is in the human capacity to bring bias into decisions and analyses, while claiming to be objective and unbiased. Using appraisal valuation to illustrate, it is worth remembering that clients often come to appraisers, especially in legal or accounting settings, with specific views about what they would like to see in their valuations, and want affirmation of those views from their appraisers, rather than the objective truth. A business person valuing his or her business, ahead of a divorce, where half the estimated value of that business has to be paid out to a soon-to-be ex-spouse, wants a low value estimate, not a high one, and much as the appraiser of the business will claim objectivity, that bias will find its way into the numbers and value. It is true that you can build AI systems to replicate this bias, but it will be much more difficult to convince those systems that the appraisals that emerge are unbiased.

1. Generalist vs Specialist: In the last century, we have seen a push towards specialization in almost every discipline. In medicine, the general practitioner has become the oddity, as specialists abound to treat individual organs and diseases, and in finance, there are specialists in sub-areas that are so esoteric that no one outside those areas can even comprehend the intricacies of what they do. In the process, there are fewer and fewer people who are comfortable operating outside their domains, and humanity has lost something of value. It is the point I made in 2016, after a visit to Florence, where like hundreds of thousands of tourists before me, I marveled at the beauty of the Duomo, one of the largest free-standing domes in the world, at the time of its construction. 
   
   The Duomo built by Filippo Brunelleschi, an artist who taught himself enough engineering and construction to be able to build the dome, and he was carrying on a tradition of others during that period whose interests and knowledge spanned multiple disciplines. In a post right after the visit, I argued that the world needed more Renaissance men (and women), individuals who can operate across multiple disciplines, and with AI looming as a threat, I feel even more strongly about this need. A Leonardo Da Vinci Bot may be able to match the master in one of his many dimensions (painter, sculptor, scientist), but can it span all of them? I don’t think so!
2. Practice bounded story telling: Starting about a decade ago, I drew attention to a contradiction at the heart of valuation practice, where as access to data and more powerful models has increased, in the last few decades, the quality of valuations has actually become worse. I argued that one reason for that depletion in quality is that valuations have become much too mechanical, exercises in financial modeling, rather than assessments of business quality and value. I went on to make the case that good valuations are bridges between stories and numbers, and wrote a book on the topic.
   
   At the time of the book’s publication, I wrote a post on why I think stories make valuations richer and better, and with the AI threat looming, connecting stories to numbers comes with a bonus. If your valuation is all about extrapolating historical data on a spreadsheet, AI can do it quicker, and with far fewer errors than you can. If, however, your valuation is built around a business story, where you have considered the soft data (management quality, the barriers to entry), AI will have a tougher time replicating what you do. 
3. Reasoning muscle: I have never been good at reading physical maps, and I must confess that I have completely lost even my rudimentary map reading skills, having become dependent on GPS to get to where I need to go. While this inability to read maps may not make or break me, there are other skills that we have has human beings, where letting machines step in and help us, because of convenience and speed, will have much worse long term consequences. In an interview I did on teaching a few years, I called attention to the “Google Search” curse, where when faced with a question, we often are quick to look up the answer online, rather than try to work out the answer. While that is benign, if you are looking up answers to trivia, it can be malignant, when used to answer questions that we should be reasoning out answers to, on our own. That reasoning may take longer, and sometimes even lead you to the wrong answers, but it is a learned skill, and one that I am afraid that we risk losing, if we let it languish. You may think that I am overreacting, but evolution has removed skill sets that we used to use as human beings, when we stopped using or needing them, and reasoning may be next on the list.
4. Wandering mind: An empty mind may the devil’s workshop, at least according to puritans, but it is also the birthplace for creativity. I have always marveled at the capacity that we have as human beings to connect unrelated thoughts and occurrences, to come up with marvelous insights. Like Archimedes in his bath and Galileo under the apple tree, we too can make discoveries, albeit much weighty ones, from our own ruminations. Again, making this personal, two of my favorite posts had their roots in unrelated activities. The first one, Snowmen and Shovels, emerged while I was shoveling snow after a blizzard about a decade ago, and as I and my adult neighbors struggled dourly with the heavy snow, our kids were out building snowmen, and laughing.  I thought of a market analogy, where the same shock (snowstorm) evokes both misery (from some investors) and joy (on the part of others), and used it to contest value with growth investing. The second post, written more recently, came together while I walked my dog, and pondered how earthquakes in Iceland, a data leak at a genetics company and climate change affected value, and that became a more general discourse on how human beings respond (not well) to the possibility of catastrophes.