With the public launch of Chat GPT in November 2022, we’re all in the midst of exploration and evaluation of the impacts from the explosion of Artificial Intelligence (AI) for public consumption.
Since that Chat GPT launch, a lot of the big players have released their public offerings. These include Microsoft’s Co-Pilot, Google’s Gemini, and X’s Grok.
At The Innovation Garage, we’ve spent the last two-and-a-half years diving deep on AI tools, both on our own and with our client base teams. Early on we’ve supported client requests for educating on AI basics.
In these heavily requested AI primer sessions, with all levels of leadership, including client board of directors and various diverse industry demographics. As a result of those efforts, we have been reviewing and monitoring how our client teams are looking to adopt AI in their daily work.
In short, supporting our client base to determine if Artificial Intelligence is the real deal, or is AI just an overblown hype show?
We are subjected to continuing bombardments of AI advertising, AI content and AI generated videos. All heavily marketing AI as the next big thing. AI is now force fed in tech stacks and email tools without an “opt-in” option.
This continuous bombardment of AI in tech stack tools and social media feeds is driving continuous context switching in our brains.
If we understand the what and the why this is happening, it can set the organization off in multiple and sometimes very distracted directions. The outputs of which is causing a significant misalignment across organizations and their ability to execute on long term strategies. Each of our clients is using their own 5D organizational brain chess, trying to suss out an ideal path forward. At an organizational level, clients are ruminating on the following key questions:
- What actually is this thing called AI?
- How will it help our team in what we do?
- Will AI will replace the role of some humans in our workplace, our systems and perhaps our own individual daily work?
The approach in this article series is to provide a basic primer on AI. Coupling that primer with our best critical thinking to date. We’ll present insights of what we’ve learned so far. We will share what clients are experiencing in their own AI discovery.
We’ll also want to time travel back a ways, exploring the historical cycles of new technology. Looking at those historical cycles of technology presenting to the world as the next savior, and what actually has manifested over time with technology.
Finally, we will make some next step suggestions, on how to make your own individual determination of the ultimate value AI will (or will not) provide for both yourself and your organization.
Artificial Intelligence Primer – A Brief History and the Basics of AI.
The initial concepts of AI evolved in the early 1950’s, with the work of Alan Turning asking the question, “can machines think?” John McCarthy is generally recognized as the person who invented AI with a series of lectures in the mid 1950’s.
Over the last seventy-five (75) years those early stage concepts evolved into some of the basic Artificial Intelligence building blocks that have become familiar today.
The overarching container is referred to as Artificial Intelligence. AI contains sub domains of Machine Learning and Deep Learning. As shown in the image below:
Deep Learning is at the core what is driving today’s AI explosion. Deep learning has introduced advanced solutions of computer vision, pattern recognition and natural language processing. Deep learning is the foundation of the most all of the publicly available tools.
Agentic AI or Agent Based AI, also called generative AI is based on prompting AI tools with roles or instructions to research and analyze data, conduct an internet search or to create images. Agent based AI is the user interface and user experience (UX) of most of the publicly available AI tools offered initially free and now with a subscription based model for access.
Artificial Intelligence & Historical Cycles – What may be the historical impacts of AI technology?
It’s important to do some time traveling backwards to understand historical cycles with new technology and impacts to the workplace and industry. We touched on the importance of understanding historical cycles in an earlier article.
“In their book, The Fourth Turning, published in 1997, the authors Howe and Strauss, discuss historical cycles. In looking back almost 500 years, the authors explain why most history repeats itself every one hundred years in twenty to twenty-five year cycles that the authors call “turnings.” Most recently, in 2023, Neil Howe published his update to the earlier work entitled “The Forth Turning is Here.”
In the same way, looking at historical cycles from a past period and the human behavior observed can help inform a future prediction of the current period. To better understand the technology introductions we face today, all you have to do is go back just one turning, to the years of 2000-2005, focusing on the area of global supply chain and operations to gain some future insight.
During the early 2000’s, supply chain practitioners spent time in corporate board rooms, presenting multiple Total Cost of Ownership (TCO) analyses as part of a sourcing strategy.
If you participated in those sessions, the dynamics in those meetings frequently went like this:
- If a local supplier could not meet the desired buy price, a procurement team conducted a sourcing activity to find the lowest-cost provider.
- Many of those low-cost providers were early-stage companies usually located in the Asia Pacific (AP) region. Almost every time, the Total Cost of Ownership analysis indicates a slight to moderate savings of offshoring compared to localization.
- However, due to price pressures and public demand of not being concerned with where the product was manufactured, the off shore decision was made. People wanted cheap goods, and didn’t really care where the goods came from.
- During this period, a pressure based decision was made by executive leaders of many organizations to ship the technology and manufacturing overseas. The supply chain historical cycles analogy is now repeating itself. This time, the decision is with AI.
- Instead of outsourcing technology and manufacturing, AI tools are being positioned to outsource a humans ability to think critically, communicate effectively and to build their own independent and meaningful skills that provide value to others.
- If organizational leaders have a minimal concern from where the work comes from, the decision cycle will repeat itself and AI adoption and integration will appear to be the best and lowest cost decision for the organization to remain competitive in the marketplace.
We predict that historical cycles will show that outsourcing of critical thinking to AI will, over time, prove out to be a poor choice. The hangover from decisions being made today will manifest in the population in five (5) to ten (10) years down the road. The trends indicate that the average person and most all of their organizational leaders will not apply critical thinking in their choices.
Most of the population will be more than happy to outsource their critical thinking to AI. It’s popular and easy to do. Outsourcing to AI avoids conflict, difficult communications and tough decisions for both the individual person and the organization. These decisions are now a very easy and extremely comfortable approach to take.
Artificial Intelligence in the Real World – What is the AI business model?
Is AI the next transformative technology, or the next rug pulling grift? To gain some insight let’s take a look at some recent statistics on the topic.
- In a recent article by Jason Lemkin he indicates that data shows that AI startups burn through cash twice as fast as previous startups.
- PE firms have poured over 1 Trillion dollars in Artificial Intelligence over the last twenty four (24) months.
There is a standard business model that for Artificial Intelligence which is starting to manifest:
- Free access for a limited time, then “hook the user” with low cost initial subscriptions and subsequent future price increases or “free to use” with embedded advertisements. A free access with advertisements model will manifest soon. This will be necessary to recover the costs of the freely available models.
- Private Equity (PE) firm’s offer up a pile of cash to build up an initial user base, then once a threshold and set point is reached, creating sticky dependencies with the user base. Changing the free model to subscriptions for recovery of the initial investment and recovery of the PE cash burn that built up the initial user base.
- Most executive leaders do not yet understand that the AI enterprise subscriptions they are signing up for do not include unlimited access to AI tokens. This will manifest in subscription prices exceeding the initial investment that was forecasted as part of the subscription.
- Lastly, it’s the data and its privacy. AI tools are being fed critical and sensitive internal data without concern of the team. This data, once important and guarded by the organization is being used to train the AI model to which the subscription fees are being paid.
Given the above elements, our guidance is to proceed with extreme caution in the evaluation of AI tools for your organization.
Artificial Intelligence and Cognitive Debt – Studies Indicate the Cognitive and Emotional Debt is Real.
To continue the historical cycles analysis with technology adoptions, we’ll travel back about fifteen (15) years to look at the next component.
The Smart phone came on the scene on a massive scale in 2010. Studies have pinpointed that with the advent of the smartphone, we actually have already experienced significant cognitive decline. In a similar vane, with that the arrival of social media around the same time, studies indicate a similar debt has manifested. Our personal connections with others have diminished and for many people, the feeling of isolation has actually increased.
Evidence is now demonstrating that heavy Artificial Intelligence use results in increasing “Cognitive Debt.” Several recently published studies are indicating that even light AI use creates cognitive debt that only increases for those that become heavy users. To learn more visit these links below:
https://www.media.mit.edu/publications/your-brain-on-chatgpt
https://time.com/7295195/ai-chatgpt-google-learning-school
To gain your own understanding of what cognitive debt is, without the aid of technology, consider these three simple questions:
- From memory, can you recite the top three (3) to five (5) phone numbers of your closet family or friends?
- What direction is North, South, East and West from your current location?
- Do you have access to and would you know how to read and navigate from a printed map?
If you can answer the above without use of technology, you would likely be in the minority. Most of these previous life skills of easy numeric recall and self navigation have evaporated for the majority of the population indicating a cognitive debt exists.
As one recent and practical example, in group discussion sessions within our programs we continually learn from our AI education program attendees. Some participants elect to that share their own personal narratives and relationships with tools like Chat GPT. Essentially, many attendees are looking to use Chat GPT in the role of a personal life coach or as an easy access virtual therapist for emotional and mental support. They each describe a process of how they have, in a way, built a personal relationship with the AI agent for support and coaching.
Over time the session attendees have placed the AI agent in a role of both a close friend and life coach. Providing daily guidance on the ideal responses to every day situations, such as crafting replies to challenging or high emotion emails received. Multiple attendees describe to us how the AI agents have over time, “do a good job of understanding me as a person.” Take from these real world AI commentaries what you will and create your own observations.
In summary, these use stories we are witnessing first hand, are a very similar (and discomforting to us) psychological trend similar to smart phones and social media.
We see this narrative across all the various personas and industry domains of the many leaders that are leaning into heavy use of AI in their daily work.
Engaging Artificial Intelligence – Critical Thinking and Small Experiments are Key.
If you elect to engage an experiment with AI, as a basic foundation and a first rule, look to conduct simple and small scale experiments. As a second rule, don’t give up your critical thinking and unknowingly make a decision that will, over time, outsource your very own critical thinking skills to AI.
Critical thinking at its essence, is the ability to have two competing thought paths in your mind at exactly the same time. At the simplest personal choice level, there is tension among three elements. Is this choice good for me? Is this choice bad for me? Or perhaps, could both be true?
Critical thinking allows you to evaluate pros and cons of decision paths in parallel and make the best decision based on your own research and conclusions.
For your own thought experiment, apply your critical thinking skills and ask yourself the following series of thought starter questions of adopting AI as a technology:
- Do you understand what your Artificial Intelligence models being considered are actually trained on? Look to understand the basics of AI and what input data is used to train the AI model being considered. Most all publicly available AI tools have their historical knowledge base established by scraping the internet and social media feeds. Understand that the simple saying of “garbage in = garbage out” applies with AI training models.
- “Is the potential “cost” of your Artificial Intelligence engagement decisions something you will be happy with in the long term?” Most AI early adopters and super users pay a price and gain speed in the short term. It is clear they likely they do not understand nor are factoring in the long term cost and potential risk of these decisions.
- Are you engaging with Artificial Intelligence because everyone else is, or have you discovered where AI can truly help both your individual and organization’s skill sets? Be mindful of the peer pressure to be part of the “in” crowd and the damage that may result if you elect to engage heavily with AI.
- What Artificial Intelligence experiments has your organization conducted? Experiment and learn how to prompt. In this way, as you conduct your own prompting experiments, you can make your own determination on value of AI for yourself and your organization.
- Are you about to make a significant Artificial Intelligence investment? If you or your organization is considering investing in AI, look for the presence of the important standard and classic business case elements of ROI and look for proven use cases before investing. Avoid shiny object syndrome at all costs when considering an investment with AI.
As a final rule and recommendation, continue to ask the above questions of both yourself and your team on a recurring and cadenced basis to keep course correcting as you learn more about AI.
Embracing Artificial Intelligence – Trends and Countermeasures to Explore.
We offer the following observations and countermeasures for AI consideration and investigation. This is both at the individual and organization level. If you’re going to use and engage with AI, make sure to use and validate AI technology as a meaningful amplifier of your analog and human centered methods. Look to ensure human interactions and craftsmanship remain in your internal working processes and your organization’s long term strategy.
First, we do see a few Artificial Intelligence trends that are emerging, here’s a quick list of elements we encourage you to explore:
- Internet Search – AI tools do a good job of finding results faster than traditional search. This works especially well if searching for products to purchase. You will likely see the significant reduction of the traditional internet search we have become accustomed to. You are already witnessing the embed of AI tabs in internet search browsers and AI enhancements in typical tech stack internet search tools.
- Research Validation – AI research and insight does provide alternate references and insights, if as mentioned above, you apply traditional analog research approaches first. Then, validate the AI results as a secondary step. Sequencing your workflows in this fashion provides alternate paths to explore outside of your initial inputs. Remember to always independently validate the results of your AI efforts.
- AI backlash – User reports of incorrect information, analysis errors, hallucinations, code errors, difficult and impossible to rework AI vibe coding, errors in vibe coding modifications and high subscription fees (i.e, the rug pull) are now being reported. These are real data points that point to the Snake Oil component of AI.
- AI will likely be a helpful scapegoat for some – AI will be used by many leadership teams as an excuse to justify job reductions and mass layoffs. AI will be used as air cover for poor information and analysis and likely an excuse by some leadership for poorly run organizations and operational efficiency.
- Human Interaction will win the day – With the AI backlash starting, this means that offering human interactions becomes the ideal countermeasure in response to the present day AI obsession. Your organization and your customer base wants and needs human interaction and they will thank you for it.
Second, experimenting with AI in small doses and continually reviewing the AI tool outputs is a good practice to become adept at. Contrast the AI outputs against a well designed and well understood human centered process. As mentioned earlier, don’t forget the business fundamentals and the return on investment for your future Artificial Intelligence investment decisions.
Third, and most importantly. Analog methods first and always. Celebrate and maintain your human interactions and process design. Don’t give up on human craftsmanship. Keep your analog and human centered approaches in what you do and if we can be so bold, double down on them.
Artificial Intelligence Prediction – Technical Savior or Carnival Snake Oil?
Our prognosis and the verdict thus far. Don’t believe the AI hype. Most AI is much more “Snake Oil” than technical savior. Most AI software solutions we have seen have been cleverly dressed up and rebranded as AI or “Agentic” agent based tools.
Many of these platforms, work in exactly the same way in which they always did. Now presented with a refined and polished AI marketing tagline. The refined tagline does little to the underlying code and functionality of these tools. It’s primarily a new and sparkling coat of marketing paint with AI racing stripes.
As a technical savior, our experiments, observations and historical insight indicate that much of AI is a “this too shall pass” solution. Using our own critical thinking from the presented analysis, and our understanding of historical cycles, we are seeing continued negative evidence that informs a bold prediction.
The prediction is that, for the most part, Artificial Intelligence is an overblown hype show.
Our prediction is that the continued expanse of Artificial Intelligence in the public domain, will do nothing else beyond creating more technical, cognitive and psychological debt for the general population. In summary, as a global culture, we will continue to see a long term increase in human mental, cognitive and emotional fallout.” – The Innovation Garage
To read more on this point, we encourage you to pick up a copy of “AI Snake Oil” by Arvind Narayanan & Sayash Kapoor. We also recommend that you consider subscribing to the authors substack page as well.
So, recall the carnivals and county fairs of your past and the snake oil carnival barker selling their “cure-all” remedy. If you’re jumping into an AI tool acquisition and potentially significant investment for the sake of being fashionable and on the leading edge with AI, be prepared to be disappointed and note the following:
- AI will not fix an organization’s culture.
- AI will not fix a broken process.
- AI usage will continue to be creatively monetized by the service providers.
- Heavy AI Users will become overly dependent on AI to do what once was a typical human cognitive task.
- The publicly available tools are, for the most part the next mainline version of the technical dopamine machine.
- Remember that you and your prompt inputs to AI are the product.
- Similar to internet search and the evolution of social media.
- Don’t be fooled into thinking it’s any different with AI.
What is the Ultimate Artificial Intelligence Countermeasure?
In summary, look to amplify what people in your organization do best. Which from our perspective, is building with human creativity, connection, innovation and craftsmanship to make things that never existed before.
If you’d like to learn more about AI, check out The Innovation Garage AI primer session offerings. In these session we present high level approaches that will support your organization to sensibly explore and create an effective AI strategy for your organization.
Take a first step by reaching out to us here, or drop an email to i[email protected].
At The Innovation Garage®, We help organizations grow. Providing education, tools, technology, and expert consulting in change management for strategy, innovation, and supply chain.
Guiding leaders from organizations across the world to intentionally self-disrupt their offerings and organizations. We deliver world-class education, tools, and technology on how to craft business operating systems focused on long-term profitable growth.