Executive Summary: "AI" has the same problem as social media: we created a class of solutions without a problem definition. Because we lack a formal definition of "artificial intelligence," we have also created the risk that anyone can package anything as "AI" with spurious accuracy and intentions. Every tool follows the same simple equation: Input + Process = Output. Taking the output of a probabilistic model at face value without critical thinking leads to poor productivity and investment allocation.
I am an early adopter of new gadgets. So, playing with ChatGPT early on was fun. It appears to be a strong language model that is taught to speak well. It acts as a good “thought partner” replaying previously fed information in different language formats if we pose nebulous questions. As a user, it's a very confident and opinionated search engine. As a technologist, it is a monumental achievement to create a conversation engine that can understand a myriad of inputs and communicate with anyone easily.
I hope we can all acknowledge a simple reality that GenAI’s “main purpose is to use natural language processing to understand and generate human-like responses to text-based queries,” as explained by ChatGPT. So, if the tool itself tells us it is a language model and is not “thinking,” then why are professors and testing institutions asking ChatGPT to take the BAR exam or SAT tests? ChatGPT is a conversational search engine that remembers previously fed information. If we search appropriately, Google Search will return the information to construct answers to SAT questions and BAR exams as well.
This brings us to the real conundrum: Why is ChatGPT passing the Turing Test even as a language processing research model?
Alan Turing, considered the father of the computer, postulated that it is not logical to ask whether a machine is intelligent. It is true. We have had intelligent machines for millennia. A mechanical trigger based on surrounding conditions is an intelligent machine. The Turing Test assesses the tester to understand whether the tester considers the subject’s responses human-like.
This is the theme that worries most people about “artificial intelligence.” We see institutions predicting that hundreds of millions of jobs will see deep impact based on a language model's testing, while search interest and corporate hype surge exponentially.
“AI” has the same problem as social media, but at a bigger amplitude. We created a solution in social media without a problem definition. It became a glorified sales and marketing vehicle because we couldn’t figure out a more relevant need for it. Tricks around keyword choices and provocation of herd mentality supersede the quality of content or truth on social media. The biggest social media companies have invested increasing dollars and staff to curb destructive behavior with limited success.
On that note, what is our problem definition that “artificial intelligence” will solve?
The first option: Do we want to just automate the most mundane tasks? But we have automated mundane tasks for centuries. A mousetrap is an automated mechanical device.
The second option: Do we want “AI” to predict the future based on information fed into it by humans? We could predict probabilistic outcomes for a long time. We have had increasingly faster and easier access to such predictions. Look at modern weather forecasts: they are immaculate.
These first two options have existed for a long time, and we haven't bothered using the term “AI.” There is a third option. But we are far away from deploying it.
The third option: Do we want “AI” to make predictions and even make decisions where a machine has the freedom to access any information on the planet? But we would need advanced robotics for a machine to gather input from the online and offline world. We need global alignment to allow a machine to have such access, which seems highly unlikely.
So, which option are folks talking about when we use the term “AI”? The truth? We have absolutely no idea. And this highlights our first problem: What is the formal definition of “artificial intelligence”?
The lack of a good definition leads to the second problem. Anyone can package anything as “AI” and try to sell it. Customers don’t know what “AI” is. Large language models, which are clearly not thinking, pass the Turing Test with folks with advanced degrees because we train society to trust popular ideas and we don't internalize what objective testing implies. So, our second problem is that there is nothing to protect customers and investors from a bevy of so-called “AI” solutions marketed to monetize vaporware with spurious accuracy and intentions.
Would we handle a butter knife and a sushi knife the same way? No. A sushi knife is extremely sharp and requires special handling.
Alan Turing may be correct that our reaction to technology is a test of its intelligence. But it is also a reflection of human critical thinking skills. To master increasingly powerful tools, we must train to understand why they work the way they do and become smarter, savvier, and more analytical. Otherwise, we will be misled while handling something we don’t understand. Remember, people paid to get tea leaves interpreted because charlatans popularized it!
Every living thing or object, including technology, follows the same simple equation: Input + Process = Output.
We control tools if we understand the inputs that go into creating them and the integrity of the process that transforms those inputs into outputs. Taking the output of a predictive model at face value without critical thinking leads to poor productivity and investment allocation on the docile end. The implications on the harmful end are unthinkable.
Social media’s origin and impact offer a two-decade lifecycle view of "AI" pitfalls awaiting us. It's worth reiterating that social media was a product without a tangible problem definition. It amplified disinformation and misrepresentation to a level where our productivity growth is lower than it has been in decades. Citizens of most countries live in divisive environments because folks with selfish intentions figured out how to turn citizens against citizens through effective segmentation. How can we prevent a further escalation of these trends with “AI”?
We can choose to be a master of tools during this “AI” wave.
First, ensure that we internalize the answer to these three fundamental questions to protect ourselves from touching misleading "AI" tools or overbuying tools we don't need:
Think about it. If we curl 30lb weights, would we buy 60lb weights just because we live next to Dwayne "The Rock" Johnson?
Second, let's embrace humility and put ourselves through a two-point critical thinking test to see how ready we are to use the “AI” tools we consider useful to avoid being taken for a ride by pretenders:
Try to apply this two-point test in our daily lives. Imagine sitting down with a friend. Imagine interviewing a candidate. Imagine listening to a TV anchor. Imagine reading the newspaper. Practice these questions.
As we explore generative models, let’s remember these facts.
First, avoid treating “AI”-branded tools as a godsend simply because they are presented with confidence and the tools themselves lack a critical human quality: self-doubt.
Interacting with confidence without self-doubt requires critical thinking skills.
Second, understand the inputs. GenAI tools have a ceiling: the quality of information they are fed. Public domain information with significant followership represents mediocrity and mainstream, not cutting edge or exceptionalism. Everything in the public domain is an interpretation of reality. Articles, white papers, and scientific papers are all peppered with author biases. The same constraints apply for using GenAI in company settings.
If a popular answer is incorrect, GenAI will present the incorrect answer with confidence.
Third, GenAI solutions are primarily based on a paper written by Google researchers in 2017: Attention Is All You Need, which introduced the Transformer. For example, the text tools we are building are based on the idea that instead of reading text one word after another like older software, a system can weigh the importance of all words at the same time to guess what comes next.
Faster doesn’t mean it thinks any better because it still doesn’t know what any of the words mean in the real world. It's guessing based on probability.
We have been working on predictive models for a long time and we already have great practical applications like weather and traffic. It is also relatively easy to validate the outputs of algorithms for games like Chess or Go because they have fixed rules. No doubt we will continue to have valuable predictive models available for mass market applications.
But effective predictions require a large volume of inputs. Mass market applications have millions of data points to learn from. But most applications are niche. Niche use cases have spurious definitions of what ‘good’ outcomes look like, lack alignment on the process to create ‘good’ outcomes, and don't have effective definitions of inputs to feed models. Given this reality, “AI” will go through the same frenzy and uproar that social media and cryptocurrencies did, unless individuals and companies elevate critical thinking to master tools.
John Oommen is an electrical engineer turned company-level evolution architect who partners with private company investors and middle-market CEOs to mature and transform portfolio companies. He is the author of Congruence: The Playmaker's Guide to High Value Companies, creator of the underlying proprietary Congruence mindset, model, and method, and pioneer of the Playmaker Capability, which is a must-have value injection mechanism that dovetails purpose, strategy, and operations.
Over more than two decades, John has led company-level transformations, M&A integrations, and operational turnarounds across dozens of mid-market, VC and PE-backed, and Fortune 100 companies, including General Electric, Accenture, Blue Ridge Partners, and Capital One, as well as enterprise clients like Emerson, Ferguson, Cox, Avaya, and Nortek, alongside numerous mid-market companies. He specializes in addressing business-to-business market problems spanning industrial manufacturing, distribution, technology, and tech-enabled services. John holds an MBA from Northwestern University’s Kellogg School of Management, is a certified Six Sigma Black Belt, and is based in Chicago.
