SmartAB™ Wisdom #59: Let AI Calculate Your Salary... And Receive Different Paychecks Each Month – Part 1

AI answers, especially from complex models like Large Language Models (LLMs), are approximations. They are not deterministic due to a combination of factors related to their design, training, and inference processes.

And this is why I mentioned in my “AI Boogeyman” book, as well as in many of my 200+ LinkedIn posts and 18,000 tweets - that you wouldn’t want a neural network to “approximate” your monthly salary and receive different paychecks each month…

To prove my point, I presented the same question to all the five leading AI Assistants: ChatGPT, Copilot, Gemini, xAI, and Meta AI. And I did it 3 times in a row. My question was:

“Why is Lululemon such a successful brand?”.

You will find all the answers I received in Part 1 and Part 2 of this post. It will be presented to you unvarnished and without anesthetics…

You may also ask: Why 3 times? Well, I believe in the following: If it happens once, perhaps it’s RANDOM; If it happens twice, perhaps it’s a COINCIDENCE; If it happens three times… It’s a PATTERN. It captures a common way we intuitively try to make sense of the world! It's not a strict scientific rule, but it reflects how we often look for meaning and predictability in events. Here's a breakdown of what each part generally implies:

Once: Random - A single occurrence is often attributed to chance. It's just a one-off event.

Twice: Coincidence - If something happens a second time, it might make you raise an eyebrow. It could still be pure chance, but it feels a bit more noteworthy.

Three times: Pattern - When an event repeats for a third time, our minds are strongly inclined to see a pattern, suggesting there might be an underlying cause or a predictable sequence.

The saying "if it happens 3 times, it's a pattern" doesn't have a single, definitive source. It's a common proverb or rule of thumb used in a variety of contexts, from problem-solving and troubleshooting to behavioral psychology and relationships.

The idea behind it is that a single instance could be a coincidence, and two could still be a fluke. But by the third time, it's more likely that there's an underlying cause or a consistent trend at play, and it's time to take it seriously.

So, Why Does AI Approximate?

The concept of "approximation" is central to how most modern AI systems work, particularly those based on machine learning. Here's a breakdown of the key reasons why:

· The Universal Approximation Theorem

This is a core theoretical foundation for why AI, specifically neural networks, works. The theorem states that a neural network with just one hidden layer can, in principle, approximate any continuous function to any desired degree of accuracy. In machine learning, the "function" is the unknown relationship between input data and the desired output.

For example, when an AI model is trained to recognize cats in images, it's essentially learning an incredibly complex, non-linear function that maps the pixel values of an image to the label "cat." We don't know the "true" function for what makes a cat a cat, but a neural network can approximate it with enough training data.

· The Unknown Nature of Reality

In many real-world problems, we don't have a simple, explicit mathematical formula to describe the relationship between inputs and outputs. Think about a self-driving car: there's no single, elegant equation that perfectly describes how to map sensor data (cameras, LiDAR) to steering commands. Instead, the AI system learns this mapping by approximating the underlying function from a massive amount of data.

· The Need for Generalization

The goal of a machine learning model isn't just to memorize the training data. If it did, it would fail when presented with new, unseen data (a problem known as "overfitting"). By approximating the underlying function, the model learns the general patterns and relationships in the data, allowing it to make accurate predictions on new data it has never seen before.

For instance, a language model doesn't just store every sentence it has ever read. It approximates the statistical relationships between words and phrases, which allows it to generate new, coherent text.

· Computational Efficiency

In many complex problems, finding the perfect solution would be computationally impossible or prohibitively expensive. Approximation allows AI to find a "good enough" solution in a reasonable amount of time. This is especially true in fields like reinforcement learning, where an agent might need to make quick decisions in a vast and complex environment.

· Dealing with Imperfect Data

Real-world data is messy, noisy, and often incomplete. Approximating a function allows AI models to be robust to these imperfections. The model learns a general trend rather than trying to perfectly fit every single noisy data point, which helps it generalize better and make more reliable predictions.

So, AI approximates because it's a practical and effective way to solve complex problems where the underlying rules are either unknown, too complex to be explicitly programmed, or where a perfect solution is not feasible. This approach allows AI to learn from data, generalize to new situations, and operate efficiently in the real world.

Here's a more detailed breakdown of why it is so:

· Function Approximation:

Many AI models are designed to approximate complex functions. They learn to map inputs to outputs by identifying patterns and relationships in vast amounts of data. Instead of hard-coded rules for every possible scenario, they generalize from what they've seen. This generalization, by its nature, is an… approximation.

· Statistical Nature:

AI models are inherently statistical. They learn probabilities and relationships between data points. When generating an answer, they often predict the most probable next word or outcome based on their training. This probabilistic approach means there isn't always one "correct" answer, but rather a spectrum of likely ones.

· Ambiguity and Nuance In Data:

Human language and real-world data are inherently ambiguous and nuanced. There's often more than one way to interpret a question or situation, and different "correct" answers can co-exist depending on the context or desired emphasis. AI models reflect this variability.

· Incomplete Information:

AI models learn from the data they are trained on. This data is a sample of the world, not a complete representation. Therefore, models may not have all the information needed to give a perfectly precise answer, leading to approximations.

· Satisfying, Not Optimizing:

In many AI applications, the goal isn't necessarily to find the absolute best solution, but a "good enough" or "satisficing" solution. It might be computationally much harder to get a perfectly optimal answer than an approximately optimal one, and for many real-world problems, an approximation is perfectly acceptable.

Even with the same input, you might get slightly different answers from an AI model, especially generative ones. This non-deterministic behavior also stems from several Computational Factors:

· Random Initialization:

The initial weights of neural networks are typically randomized. This means that even with the same training data and architecture, different training runs can lead to slightly different models.

· Stochastic Gradient Descent (SGD):

Many AI models are trained using variants of SGD, which updates model parameters based on small, randomly sampled batches of data. This introduces randomness into the optimization process.

· Dropout:

Dropout is a regularization technique that randomly "drops out" (ignores) a percentage of neurons during training. This prevents overfitting but also introduces randomness.

· Data Augmentation:

Techniques like data augmentation randomly transform training data (e.g., rotating images, shuffling sentences). This increases model robustness but also adds variability to the training process.

· Random Seeds:

While you can try to control for randomness by setting a "random seed," this doesn't always guarantee exact reproducibility across different hardware or software environments due to other factors.

· Floating-point Precision:

AI models perform numerous calculations using floating-point numbers. Small rounding errors due to limited precision can accumulate and lead to slight variations in output.

· Parallel Processing:

AI models often run on powerful hardware (like GPUs) that perform operations in parallel. The exact order of these parallel operations is not always guaranteed, which can introduce subtle differences.

In essence, the "approximation" aspect relates to the model's fundamental goal of generalizing from data, while the "non-deterministic" aspect refers to the inherent randomness introduced during both the learning (training) and output generation (inference) phases. This balance allows AI models to be creative, adaptable, and perform well in complex, uncertain real-world scenarios, even if it means sacrificing perfect, predictable consistency.

If you ask the same question multiple times, you might get slightly different responses each time. That’s because the model doesn’t pull from a fixed database—it constructs answers dynamically. Even with the same input, small variations in internal processing can lead to different outputs.

And Why Do We Think This Way?

Our brains are wired to identify patterns. It's a fundamental aspect of learning, problem-solving, and survival. Recognizing patterns allows us to make predictions, understand relationships, and develop strategies. This saying reflects that innate drive to find order in what might initially seem chaotic. While useful for everyday thinking, it's important to remember this is a heuristic, not a guaranteed truth.

· Confirmation Bias:

We might be more likely to notice the third occurrence if we're already looking for a pattern after the first two.

· Small Sample Size:

Three instances are still a very small sample size, and true randomness can produce streaks that look like patterns. For example, flipping a coin three times and getting heads each time doesn't mean the coin is biased.

· Actual Patterns:

Of course, sometimes it is a genuine pattern! The saying helps us identify when to start investigating further.

So, while not a rigorous scientific principle, it's a handy rule of thumb for how we often interpret repeated events in our lives! Similarly, the famous Six Sigma 5 Whys technique is a simple and often effective tool for identifying potential root causes, but it does not guarantee finding the absolute, single, or most comprehensive root cause in every situation. Here's a breakdown of the 5 Whys strengths and limitations:

5 Whys Strengths:

Simplicity and Ease of Use: It's very straightforward to learn and apply, even for those without extensive training in root cause analysis.

Encourages Deeper Thinking: By repeatedly asking "why," it pushes individuals or teams beyond superficial symptoms to explore underlying reasons.

Cost-Effective: It doesn't require specialized software or complex methodologies, making it accessible for many types of problems.

Focuses on Process, Not Blame: The aim is to understand why something happened, not who is responsible, fostering a constructive problem-solving environment.

Effective for Simple to Moderately Complex Problems: For issues with a clear, linear cause-and-effect chain, it can quickly lead to actionable insights

5 Whys Limitations:

Can Stop at Symptoms: There's a tendency for investigators to stop too early, before reaching the true underlying cause, especially if the answer seems "good enough." The "five" in 5 Whys is a guideline, not a strict rule; sometimes more or fewer "whys" are needed.

Limited by Investigator's Knowledge: The effectiveness heavily relies on the knowledge and experience of the people conducting the analysis. If they don't know enough about the system or process, they might not be able to identify the correct causal links.

Focus on a Single Causal Chain: The traditional 5 Whys often leads to a single root cause. In reality, many problems have multiple contributing factors or a complex web of causes, which the linear nature of 5 Whys might miss.

Not Always Repeatable: Different people applying the 5 Whys to the same problem might arrive at different root causes, indicating a degree of subjectivity.

Lack of Empirical Validation: It primarily relies on deduction and doesn't inherently require empirical evidence or data to support the identified root cause. This can lead to assumptions or inaccurate conclusions.

Can Lead to Blame (if not facilitated correctly): While the intent is to avoid blame, if not carefully managed, the questioning can sometimes inadvertently point fingers at individuals rather than systemic issues.

Difficulty with Complex Systems: For highly complex problems involving intricate interactions between various components, processes, and human factors, 5 Whys can oversimplify and fail to capture the full picture.

The 5 Whys is excellent as a first step for many problems, especially in Lean and continuous improvement initiatives. It's great for:

· Minor or recurring issues.

· Getting a quick understanding of a problem.

· Engaging teams in problem-solving.

However, for more critical, complex, or high-risk problems, it's often beneficial to use the 5 Whys in conjunction with or as a starting point for more robust root cause analysis tools, such as:

· Fishbone (Ishikawa) Diagrams:

These diagrams help visualize multiple potential causes across different categories (Man, Machine, Material, Method, Measurement, Environment).

· Fault Tree Analysis (FTA):

A top-down, deductive failure analysis that maps out the logical relationships between various events that can lead to a top undesired event.

· Failure Mode and Effects Analysis (FMEA):

A systematic approach to identify potential failure modes in a system, product, or process, and their causes and effects.

· Event and Causal Factor Charting:

A structured method for analyzing incidents by mapping out the sequence of events and the causal factors.

The 5 Whys is a valuable tool for initiating root cause analysis and uncovering immediate causes. However, for a guaranteed and comprehensive understanding of complex issues, it often needs to be complemented by AI methodologies…

Frankly, I could write another book on this subject and list hundreds of reasons for your consideration. However, instead of doing so, I decided to offer you a concrete example by asking the most popular conversational AI assistants exactly the same questions THREE times and show you what transpired. Remember, my question was: “Why is Lululemon such a successful brand?”…

ChatGPT - First Run:

Lululemon’s success comes from a mix of smart brand strategy, high product quality, and a strong community-driven business model. Here’s a breakdown of why it stands out:

1. Premium Product + Performance Focus

• High-Quality Materials: Lululemon's proprietary fabrics like Luon, Nulu, and Everlux are known for comfort, stretch, and moisture-wicking properties.

• Product Innovation: They invest in R&D (e.g., “Whitespace Lab”) to constantly improve performance gear.

• Durability: Customers often justify the high price because the products last longer and perform better than cheaper alternatives.

2. Strong Brand Identity

• Lifestyle Brand: Lululemon sells more than clothes—it sells an aspirational lifestyle centered on health, mindfulness, and personal development.

• Premium Positioning: By pricing high and maintaining limited discounting, it stays exclusive and desirable.

3. Community & Culture

• Local Ambassadors: They partner with yoga instructors and fitness professionals to build grassroots brand loyalty.

• In-Store Experiences: Stores often host yoga or fitness classes, creating a community hub rather than just a retail space.

• Employee Culture: Lululemon invests heavily in employee development and personal growth, which boosts internal morale and external brand perception.

4. Direct-to-Consumer Mastery

• Controlled Retail: Most sales are through Lululemon’s own stores and website, allowing it to control the customer experience.

• E-commerce Growth: Lululemon adapted well to online retail, especially during COVID-19, with strong digital infrastructure.

5. Smart Expansion

• Menswear Growth: Originally a women’s brand, Lululemon has successfully grown its men’s line—a tough market to crack.

• International Markets: It’s expanding into Asia and Europe strategically, maintaining brand integrity while scaling.

• Category Expansion: Beyond yoga pants—into running, training, accessories, footwear, and even tech (e.g. Mirror acquisition).

6. Cult-like Brand Loyalty

• Customers become loyal advocates. Many people feel a personal connection to the brand and its values around wellness and empowerment.

Lululemon is successful because it combines performance apparel with lifestyle branding, community engagement, and premium positioning—delivering a product and experience that feels personal and aspirational

ChatGPT - Second Run:

Lululemon's success as a brand can be attributed to a combination of smart product strategy, strong community building, and excellent brand positioning. Here's a breakdown of the key reasons behind its success:

1. Premium Product with Strong Identity

• Quality and Performance: Lululemon is known for high-quality, durable, and technically advanced fabrics (e.g., Luon, Nulu). Customers often find their products worth the premium price.

• Innovation: The brand invests heavily in research and development to stay ahead in technical apparel, often launching proprietary fabrics and features.

2. Lifestyle Branding

• Beyond Apparel: Lululemon doesn’t just sell clothes; it sells a lifestyle centered around wellness, mindfulness, and athleticism.

• Aspirational Image: Its brand identity is aspirational but accessible, appealing especially to health-conscious, urban consumers.

3. Community and Culture

• Ambassadors and Events: Lululemon partners with yoga instructors, athletes, and local fitness influencers. They host free yoga classes, running clubs, and community events to build a loyal following.

• In-Store Experience: Stores often act as community hubs rather than just retail spaces. This fosters loyalty and brand engagement.

4. Customer Loyalty and Word of Mouth

• Cult-like Following: Many Lululemon customers are highly loyal. The combination of quality, fit, and brand identity leads to strong word-of-mouth marketing.

• Consistent Fit and Style: Their sizing and fit are consistent, which builds trust and repeat purchases.

5. Smart Retail Strategy

• Scarcity and Exclusivity: Limited releases and frequent rotation of new products create urgency and a “get it before it’s gone” mentality.

• Vertical Integration: Lululemon controls most of its supply chain, which helps with quality control and margins.

6. Expansion into New Segments

• Men’s Apparel Growth: Originally focused on women, Lululemon has made a strong push into men's activewear, seeing significant growth.

• Diversification: They’ve also moved into footwear, personal care, and accessories.

• Digital Growth: Their online sales and digital community-building efforts have been highly effective, especially during and after the pandemic.

7. Strong Corporate Culture and Leadership

• Mission-Driven Company: The brand’s focus on personal development and well-being resonates internally and externally.

• Leadership: Strategic leadership decisions have kept the brand agile and relevant amid changing consumer habits.

ChatGPT - Third Run:

Lululemon is a highly successful brand because it has mastered the intersection of premium activewear, lifestyle branding, and community engagement. Here's a concise breakdown of why it's so successful:

1. High-Quality, Technical Products

• Fabric Innovation: Lululemon designs proprietary fabrics like Luon and Nulu, offering comfort, stretch, and moisture-wicking.

• Fit and Function: Their apparel is tailored with athletes in mind—especially yoga, running, and training—resulting in loyal customers who trust the fit and performance.

2. Powerful Brand Positioning

• Lifestyle Over Apparel: Lululemon isn’t just a clothing company—it represents a lifestyle centered around wellness, mindfulness, and self-improvement.

• Premium Image: Positioned as a high-end brand, Lululemon charges premium prices, reinforcing a sense of exclusivity and quality.

3. Community-Driven Strategy

• Ambassadors & Local Events: Lululemon partners with fitness instructors and hosts free yoga sessions and run clubs, turning customers into brand advocates.

• In-Store Experience: Their stores feel more like community hubs than retail outlets—encouraging deeper customer loyalty.

4. Customer Loyalty

• Strong Repeat Business: The consistent quality, fit, and feel of Lululemon products create trust—and frequent repeat purchases.

• Word-of-Mouth Growth: Their passionate fanbase organically spreads the brand through social media and everyday life.

5. Smart Growth and Expansion

• Men’s Line and Beyond: Originally for women, Lululemon is expanding rapidly into men’s wear, footwear, and self-care.

• Global and Digital Focus: They’ve scaled internationally while also investing heavily in e-commerce and app-based experiences.

6. Vertical Integration and Control

• Lululemon owns much of its supply chain, which means better quality control, product innovation, and profit margins.

Lululemon is successful because it sells more than just clothes—it sells identity, belonging, and self-confidence, all backed by quality products and smart branding.

In Part 2 of this post, I will present you with the triple answers I received from Copilot, Gemini, xAI, and Meta AI. In all cases, I asked exactly the same question 3 times, and the responses were accurately recorded and presented in my posts…

For More Information

Please see my other posts on LinkedIn, X, Substack, and CGE’s website.

AI Boogeyman

You can also find additional info in my hardcover and paperback books published on Amazon: “AI Boogeyman – Dispelling Fake News About Job Losses” and on our YouTube Studio channel...

SmartAB™ - SUBSCRIBE NOW

A Radically Innovative Advisory Board Subscription Services...