🖍️Alan Watkins - Collected Quotes

"A major aspect of the alignment problem is values misalignment: AI systems may lack sufficient understanding of human ethics, cultural norms, or social contexts, making it difficult to translate our values directly into algorithms. For instance, if an AI is programmed to prioritise efficiency without balancing safety or ethical considerations, it might make decisions that, while effective, disregard human welfare. This is further complicated by 'specification gaming', where an AI might exploit loopholes in its programming to achieve objectives in unintended or counterproductive ways. An AI instructed to avoid obstacles, for instance, could redefine what counts as an 'obstacle' and take routes that, while technically following the rule, lead to negative outcomes." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"A transformer is a type of deep learning model designed to handle sequential data, such as text, more efficiently than previous RNNs. It uses a self-attention mechanism introduced [...] to process all parts of a sequence simultaneously, rather than one by one. This allows it to capture relationships between words (or data points) more effectively. The development of transformers revolutionised natural language processing and was a key leap forward. Transformers also used an encoder–decoder architecture. The encoder transforms the input into an abstract representation, and the decoder generates the output. So, by tokenising text into subword units and using parallel self-attention, it enables far greater throughput than RNNs. This design eliminates the need for recurrence (in RNNs) and achieves state-of-the-art results in tasks like translation and allows for a 1,000× speed-up using GPUs." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"An alternative future is a more federated open-source AI where commoditised LLM can be run independently by anyone but loosely coordinated between each other if that is mutually advantageous. This requires sophisticated governance of an ecosystem where the rules that maintain the healthy stability of the ecosystem are defined and adhered to. This model allows anyone to put any LLM on a normal computer and plug into the AI. This is the path that already seems most sensible to business leaders and government officials, who recognise that their private data and iterative datasets hold value that can increase productivity and improve the bottom line or assist citizens in their lives." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"Driven by a fear of missing out (FOMO), many companies have launched AI initiatives. But many failed. For example, customer service chatbots have been introduced to handle inquiries, reduce costs and improve efficiency. But chatbots failed to grasp the complexity of customer issues; they created frustration rather than solving problems. Customers were stuck in repetitive loops, being asked the same questions, and unable to reach a human when needed. Instead of enhancing the customer experience, they delivered spikes in customer complaints instead." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"In game theory, the prisoner’s dilemma provides a powerful framework for understanding why companies might hesitate to adopt AI, even if it appears advantageous in the long run. For example, two competitors might invest in AI-driven automation to gain a potential edge, but if they do they also incur increased costs. Or they could both refrain from adopting AI-automation and avoid the associated risks and expenses. The dilemma arises because each company fears that if it chooses a different path to its competitors, it could be at a significant disadvantage, losing market share, efficiency, or innovation capability." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"In RAG methods, the AI model itself doesn’t actually 'remember' or learn the proprietary data directly. Instead, the proprietary data are stored separately in what’s called a vector database. When the model is asked a question, it first performs a quick search of the proprietary database, finds relevant pieces of information, and then uses these to generate its response. The model’s core parameters remain entirely unchanged and are never updated with this private data. In this sense, the model hasn’t learned or 'seen' your proprietary data in its internal parameters, it only temporarily consults it as a reference to formulate an answer." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"One major concern is goal misalignment, where an AI interprets its goals in unintended ways. This demonstrates the challenge of designing objectives that are both specific and safe and consider the wider context. Another issue is instrumental convergence, the idea that an AI might develop certain intermediate goals, like acquiring resources or ensuring its survival, that help it achieve its primary objective but also make it harder to control. These concerns tie into the broader value alignment problem: the difficulty of ensuring that AI systems act in accordance with human ethics and priorities." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"So training is all about learning from data, while inference is about using that learning to make real-world predictions or classifications. Training builds the model’s capability, while inference applies that capability to new data." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"Synthetic data are artificially created data that closely resembles real-world data, generated using algorithms, simulations, or machine learning models. Unlike real data, which are collected from actual events or user interactions, synthetic data are intentionally designed to share the statistical properties and patterns of real data. This makes it valuable for training and testing machine learning models, particularly in fields where real data may be limited, sensitive, or difficult to obtain. [...] One of the key benefits of synthetic data is its ability to overcome challenges related to data scarcity and privacy. In fields like healthcare and finance, privacy regulations often restrict access to sensitive data. By using synthetic data, developers can create and train models while maintaining user privacy. Additionally, synthetic data can be generated to cover rare or unique scenarios, which may not be well-represented in real-world data, making models more robust and better at handling fringe cases." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"The AI adoption challenges that businesses are facing share a common thread: AI is not a magic solution. Many businesses fall into the trap of expecting AI to solve their problems quickly, without fully understanding its limitations or properly integrating it into their operations. Data quality plays a crucial role in the success of AI systems; if the data are biased, incomplete, or outdated, the results will reflect those shortcomings. In addition, the human element is vital. AI should augment, not replace, human intelligence in areas that require empathy, creativity, or complex decision-making, such as dealing with the many experts right across the organisation. Additionally, AI systems need to be adaptable, accounting for external factors such as cultural differences, market changes, and unpredictable human behaviours." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"This AI-driven version of the prisoner’s dilemma captures a fundamental tension in business decision-making: competitive pressures can compel firms to take actions that aren’t necessarily aligned with their best interests. It highlights the risk of acting defensively out of fear and creating an unsustainable cycle of AI investment without strategic benefit, rather than fostering cooperative approaches that could yield mutual gains, such as industry-wide standards, ethical AI practices, or shared innovation efforts."(Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"The crux of the disagreement lies in the perceived balance of risk and reward. Extinctionists stress that the stakes of getting AI wrong are so high, potentially existential, that precaution must take precedence. Expansionists, however, contend that an overly cautious approach could stifle innovation and prevent humanity from achieving its full potential, including partnering with AI to prevent alignment problems. Bridging this divide requires finding strategies to pursue the benefits of AI while addressing the legitimate concerns about its risks, a balance that continues to fuel heated debate in AI ethics and policy circles." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"The third battleground is the race to Artificial General Intelligence (AGI) – machines capable of performing any intellectual task that humans can. Although still in early skirmishes, this battle is intensifying, with leading technology companies and researchers making significant strides. [...] The race towards AGI is characterised by rapid technological advancements, differing expert opinions on timelines, ethical and regulatory considerations, and geopolitical dynamics. As AI systems become more sophisticated, the importance of responsible development and international cooperation becomes increasingly critical." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"Training is the process of teaching a model to recognise patterns and relationships in data and adjust over time to improve accuracy. During this phase, the model is fed large amounts of labelled data (data where the correct output is already known), and it tries to learn the associations between input data and its corresponding outputs. As the model processes the data, it repeatedly adjusts its internal parameters, like weights and biases, to reduce the difference between its predictions and the actual results (just like the human mind adjusts.) This iterative optimisation continues until the model reaches an acceptable level of accuracy." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

⛩️Douglas T Ross - Collected Quotes

"Automatic design has the computer do too much and the human do too little, whereas automatic programming has the human do too much and the computer do too little. Both techniques are important, but are not representative for what we wish to mean by computer-aided design." (Douglas T Ross, "Computer-Aided Design: A Statement of Objectives", 1960)

"Computer-aided design is not automatic design, although it must include many automatic design features. By automatic design we mean design procedures which are capable of being completely specified in a form which a computer can execute without human intervention." (Douglas T Ross, "Computer-Aided Design: A Statement of Objectives", 1960)

"It is very difficult to define what is meant by computer-aided design since the complete definition is, in fact, the sum and substance of the total project effort which has only begun. It is much easier to describe, what is not computer-aided design as we mean it." (Douglas T Ross, "Computer-Aided Design: A Statement of Objectives", 1960)

"The objective of the Computer-Aided Design Project is to evolve a machine systems which will permit the human designer and the computer to work together on creative design problems."  (Douglas T Ross, "Computer-Aided Design: A Statement of Objectives", 1960)

"Mechanical drawings and blueprints are not mere pictures, but a complete and rich language. In blueprint language, scientific, mathematical, and geometric formulations, notations, mensurations, and naming do not merely describe an object or process, they actually model it. Because of broad differences in subject, purpose, roles, and the needs of the people who use them, many forms of blueprint have evolved, but all rigorously present well structured information in understandable form." (Douglas T Ross, "Structured analysis (SA): A language for communicating ideas", IEEE Transactions on Software Engineering Vol. 3 No. 1, 1977)

"Structured analysis (SA) combines blueprint-like graphic language with the nouns and verbs of any other language to provide a hierarchic, top-down, gradual exposition of detail in the form of an SA model. The things and happenings of a subject are expressed in a data decomposition and an activity decomposition, both of which employ the same graphic building block, the SA box, to represent a part of a whole. SA arrows, representing input, output, control, and mechanism, express the relation of each part to the whole." (Douglas T Ross, "Structured analysis (SA): A language for communicating ideas", IEEE Transactions on Software Engineering Vol. 3 No. 1, 1977)

"The natural law of good communications takes the following, quite different, form in SA: Everything worth saying about anything worth saying something about must be expressed in six or fewer pieces." (Douglas T Ross, "Structured analysis (SA): A language for communicating ideas", IEEE Transactions on Software Engineering Vol. 3 No. 1, 1977)

"There are certain basic, known principles about how people's minds go about the business of understanding, and communicating understanding by means of language, which have been known and used for many centuries. No matter how these principles are addressed, they always end up with hierarchic decomposition as being the heart of good storytelling." (Douglas T Ross, "Structured analysis (SA): A language for communicating ideas", IEEE Transactions on Software Engineering Vol. 3 No. 1, 1977)

"We never have any understanding of any subject matter except in terms of our own mental constructs of ‘things’ and ‘happenings’ of that subject matter." (Douglas T Ross, "Structured analysis (SA): A language for communicating ideas", IEEE Transactions on Software Engineering Vol. 3 No. 1, 1977)

"A general theme for what I'm trying to convey and what actually drove me and my very industrious and creative project members over all these years, is… that there is much more to it than pictures. It has to be a picture language. There has to be meaning there, and the meaning is useful. You're trying to solve problems. So it really comes down to man machine problem solving. Better means of communication and expression is what always has driven our work." (Douglas T Ross, "Retrospectives: The Early Years in Computer Graphics at at MIT", Lincoln Lab and Harvard, 1989)

"There is a rigorous science, just waiting to be recognized and developed, which encompasses the whole of 'the software problem,' as defined, including the hardware, software, languages, devices, logic, data, knowledge, users, users, and effectiveness, etc. for end-users, providers, enablers, commissioners, and sponsors, alike." (Douglas T Ross,, 1989)

🤖Prompt Engineering: Domains (Just the Quotes)

"The idea behind transfer learning is that the pre-trained model has already learned a lot of information about the language and relationships between words, and this information can be used as a starting point to improve performance on a new task. Transfer learning allows LLMs to be fine-tuned for specific tasks with much smaller amounts of task-specific data than would be required if the model were trained from scratch. This greatly reduces the amount of time and resources needed to train LLMs." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"As the tech industry moves from non-generative models to generative models, it is shifting away from feature engineering, or creating features to model the data and experimenting with different hyperparameters to optimize performance. Generative models, and specifically LLMs, do not require feature engineering. Today, the core requirements are usually prompt engineering or building a RAG pipeline - skills that lie within the domain of AI engineers." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Context is crucial for how language models understand and generate code. The model processes your input by analyzing relationships between different parts of the code and documentation to determine meaning and intent. [...] The model evaluates context by calculating mathematical relationships between elements in your input. However, it may miss important domain knowledge, coding standards, or architectural patterns that experienced developers understand implicitly." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs,  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"In prompt engineering, we customize the prompts or questions we give the model to get more accurate or insightful responses. The way a prompt is structured has a massive impact on how well a model understands the task at hand and, ultimately, how well it performs. Given LLMs’ versatility, prompt engineering has become an important skill for getting the most out of these models across different domains and tasks. The key is to understand how different prompt structures lead to different model behaviors. There are various strategies - ranging from simple one-shot prompting to more complex techniques like chain-of-thought prompting - that can significantly improve the effectiveness of LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

 "There are three techniques for model domain adaptation: prompt engineering, RAG, and fine-tuning. Strictly speaking, RAG is a form of dynamic prompt engineering where developers use a retrieval system to add content to an existing prompt, but RAG systems are used so often that it’s worth discussing them separately. One critical difference with fine-tuning is that you must have access to the model’s weights, information that is usually not available with cloud-based, proprietary LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Generative artificial intelligence (GenAI), powered by large language models (LLMs) like Google’s Gemini and OpenAI’s GPT, has transformed how we work and live, revolutionizing business after business. Despite this success, generative AI falls short in domains where specific domain knowledge, high accuracy, and explainability are essential. And it has other significant limitations, including hallucinations and a lack of context and relations. This is where knowledge graphs (KGs) come in, provid-ing contextual information - such as experiences, environmental characteristics, cultural aspects, and social normsneeded to build the 'third wave of AI' for mission-critical applications." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"KGs are sophisticated graph structures that represent real-world entities (people, places, diseases, proteins), define meaningful connections between them, and provide context. KGs provide structured, explainable knowledge representation but are challenging to build and query; LLMs offer natural language processing capabilities but suffer from hallucinations, stale information, and a lack of domain-specific grounding. Together, they are a 'killer combination': LLMs can extract entities and relationships from unstructured text to build KGs more efficiently, providing more autonomous and powerful graph querying and analysis. Meanwhile, KGs provide reliable, up-to-date domain knowledge to ground LLM responses and prevent hallucinations." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"RAG is a paradigm that combines the strengths of LLMs with the rich, often unstructured data stored in a lakehouse. Rather than asking an LLM to generate responses purely from its internal parameters and training data, where knowledge can be outdated or incomplete, RAG systems first retrieve relevant documents, records, or data slices from your lakehouse and then feed those pieces into the model as context for its generative step. The result is an AI that can speak confidently about the latest reports, proprietary datasets, or domain-specific knowledge you have stored without having to retrain the model each time your data changes." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"Traditional paradigms build systems for specific purposes with structured, homogeneous databases. This approach works for tailored needs but is impractical for complex domains that need to adapt to user characteristics and integrate heterogeneous data. KGs capture connections, enabling relationship discovery through graph pattern matching and traversal. Both the Resource Description Framework (RDF) and Labeled Property Graphs (LPGs) provide machine-readable formats that humans can interpret. KGs emphasize rich, meaningful data representations usable by both humans and machines, enabling a paradigm shift where intelligent behavior is encoded in a unique source of truth." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

🤖Prompt Engineering: Intelligence (Just the Quotes)

"Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality." (Judea Pearl, "The Book of Why: The New Science of Cause and Effect", 2018)

"First, intelligence is situational - there is no such thing as general intelligence. Your brain is one piece in a broader system which includes your body, your environment, other humans, and culture as a whole. Second, it is contextual - far from existing in a vacuum, any individual intelligence will always be both defined and limited by its environment. (And currently, the environment, not the brain, is acting as the bottleneck to intelligence.) Third, human intelligence is largely externalized, contained not in your brain but in your civilization. Think of individuals as tools, whose brains are modules in a cognitive system much larger than themselves - a system that is self-improving and has been for a long time." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Inference is to bring about a new thought, which in logic amounts to drawing a conclusion, and more generally involves using what we already know, and what we see or observe, to update prior beliefs. […] Inference is also a leap of sorts, deemed reasonable […] Inference is a basic cognitive act for intelligent minds. If a cognitive agent (a person, an AI system) is not intelligent, it will infer badly. But any system that infers at all must have some basic intelligence, because the very act of using what is known and what is observed to update beliefs is inescapably tied up with what we mean by intelligence. If an AI system is not inferring at all, it doesn’t really deserve to be called AI." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"The idea that we can predict the arrival of AI typically sneaks in a premise, to varying degrees acknowledged, that successes on narrow AI systems like playing games will scale up to general intelligence, and so the predictive line from artificial intelligence to artificial general intelligence can be drawn with some confidence. This is a bad assumption, both for encouraging progress in the field toward artificial general intelligence, and for the logic of the argument for prediction." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"The problem-solving view of intelligence helps explain the production of invariably narrow applications of AI throughout its history. Game playing, for instance, has been a source of constant inspiration for the development of advanced AI techniques, but games are simplifications of life that reward simplified views of intelligence. […] Treating intelligence as problem-solving thus gives us narrow applications." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Agentic intelligence feels incredibly powerful in demos but breaks in production. Indeed, it is very fragile without solid infrastructure. Every day, I personally see tons of clever orchestrations around dumb prompt chains tied up in a brittle, underused LLMOps infrastructure. But building this infrastructure means acknowledging the costs: performance overhead, strict interface contracts, and state complexity, as well as a need for more LLMOps engineers to create the best practices, tooling, and frameworks to run these systems reliably, safely, and robustly." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Agentic AI extends GenAI by embedding intelligence within autonomous or semi-autonomous systems that can plan, reason, and take actions within defined boundaries. Instead of simply generating a report, an agentic system might determine which data it needs, retrieve that data from multiple sources, perform analysis, summarize the results, and then trigger follow-up workflows, all while maintaining auditability and alignment with governance policies." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"Intelligent systems connect users to AI and ML to achieve meaningful objectives. An intelligent system is one in which intelligence evolves and improves over time, particularly when it improves by watching how users interact with the system.[...] The primary objective of the intelligent system is to support users in accomplishing complex tasks - not by replacing them, but by enhancing their decision-making capabilities. [...] An intelligent system must also have the ability to learn from user interactions and explicit feedback, as well as utilize contextual information. The system should contin-uously develop, use, and maintain an evolving knowledge base. This evolution is driven not only by data sources but also by ongoing interactions with users." (Alessandro Negro et al, "Knowledge Graphs and LLMs in Action", 2026)

"LangGraph handles the perception, reasoning, and action flow while maintaining memory and context across tasks. In this sense, it functions as both the development environment and the orchestration layer - coordinating the steps of perception, reasoning, and action while managing connections to external systems. Underneath this, emerging standards like MCP ensure that agents can connect securely and consistently to tools and data sources, making agentic architectures portable and scalable across platforms. Taken together, the orchestration layer and emerging interoperability standards like MCP form the foundation for scalable agentic AI. They make it possible for agents to perceive, reason, act, and learn in coordinated ways across complex environments, translating autonomous intelligence into practical, enterprise-grade capability." (Fern Halper, "Data Makes the World Go 'Round", 2026)

🤖Prompt Engineering: Retrieval Augmented Generation [RAG] (Just the Quotes)

"As the tech industry moves from non-generative models to generative models, it is shifting away from feature engineering, or creating features to model the data and experimenting with different hyperparameters to optimize performance. Generative models, and specifically LLMs, do not require feature engineering. Today, the core requirements are usually prompt engineering or building a RAG pipeline - skills that lie within the domain of AI engineers." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs,  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

 "There are three techniques for model domain adaptation: prompt engineering, RAG, and fine-tuning. Strictly speaking, RAG is a form of dynamic prompt engineering where developers use a retrieval system to add content to an existing prompt, but RAG systems are used so often that it’s worth discussing them separately. One critical difference with fine-tuning is that you must have access to the model’s weights, information that is usually not available with cloud-based, proprietary LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Vector databases are designed to store and index high-dimensional embeddings - dense numeric vectors that capture the semantic meaning of text, images, audio, or other content. Instead of looking for exact matches, they use approximate nearest neighbor (ANN) algorithms to return the items whose vectors lie closest to a query vector in that multidimensional space. This makes them the engine behind semantic search, recommendation systems, image-or-audio similarity matching, and retrieval augmented generation (RAG) pipelines that supply LLM prompts with relevant context in milliseconds." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"In RAG methods, the AI model itself doesn’t actually 'remember' or learn the proprietary data directly. Instead, the proprietary data are stored separately in what’s called a vector database. When the model is asked a question, it first performs a quick search of the proprietary database, finds relevant pieces of information, and then uses these to generate its response. The model’s core parameters remain entirely unchanged and are never updated with this private data. In this sense, the model hasn’t learned or 'seen' your proprietary data in its internal parameters, it only temporarily consults it as a reference to formulate an answer." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"RAG applications must be built with semantics, metadata, and governance in mind. The retrieved information must be high-quality, secure, and appropriate for the user’s role. Equally important is monitoring and management: checking whether source data has changed, ensuring vector stores remain accurate, and watching for hallucinations or data leakage. Organizations are definitely starting to experiment with RAG models today; some are putting them into production applications. Some believe that using RAG helps mitigate hallucinations because it is grounded in trusted organizational data." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"RAG is a paradigm that combines the strengths of LLMs with the rich, often unstructured data stored in a lakehouse. Rather than asking an LLM to generate responses purely from its internal parameters and training data, where knowledge can be outdated or incomplete, RAG systems first retrieve relevant documents, records, or data slices from your lakehouse and then feed those pieces into the model as context for its generative step. The result is an AI that can speak confidently about the latest reports, proprietary datasets, or domain-specific knowledge you have stored without having to retrain the model each time your data changes." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"The foundational form of RAG, often called naive RAG, follows a straightforward pattern. A pipeline retrieves supporting context from external sources such as enterprise documents, knowledge bases, or structured datasets and appends that information to the model’s prompt before inference. In the most common implementation, each document is converted into an embedding, a numerical representation of its semantic meaning, using either the same foundation model or a specialized embedding model. When a user submits a query, the system performs a vector similarity search to find documents whose embeddings most closely match the query’s vector representation, and the retrieved content is concatenated with the user query before being passed to the language model." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🖍️Nick Bostrom - Collected Quotes

"A genie is a command-executing system: it receives a high-level command, carries it out, then pauses to await the next command. A sovereign is a system that has an open-ended mandate to operate in the world in pursuit of broad and possibly very long-range objectives. Although these might seem like radically different templates for what a superintelligence should be and do, the difference is not as deep as it might at first glance appear." (Nick Bostrom, "Superintelligence", 2014)

"A question distinct from, but related to, the question of kinetics is whether there will be one superintelligent power or many? Might an intelligence explosion propel one project so far ahead of all others as to make it able to dictate the future? Or will progress be more uniform, unfurling across a wide front, with many projects participating but none securing an overwhelming and permanent lead?" (Nick Bostrom, "Superintelligence", 2014)

"A system that has the intelligence amplification superpower could use it to bootstrap itself to higher levels of intelligence and to acquire any of the other intellectual superpowers that it does not possess at the outset. But using an intelligence amplification superpower is not the only way for a system to become a full-fledged superintelligence. A system that has the strategizing superpower, for instance, might use it to devise a plan that will eventually bring an increase in intelligence (e.g. by positioning the system so as to become the focus for intelligence amplification work performed by human programmers and computer science researchers)." (Nick Bostrom, "Superintelligence", 2014)

"A system might thus greatly boost its effective intellectual capability by absorbing pre-produced content accumulated through centuries of human science and civilization: for instance, by reading through the internet. If an AI reaches human level without previously having had access to this material or without having been able to digest it, then the AI’s overall recalcitrance will be low even if it is hard to improve its algorithmic architecture." (Nick Bostrom, "Superintelligence", 2014)

"An agent’s ability to shape humanity’s future depends not only on the absolute magnitude of the agent’s own faculties and resources - how smart and energetic it is, how much capital it has, and so forth - but also on the relative magnitude of its capabilities compared with those of other agents with conflicting goals." (Nick Bostrom, "Superintelligence", 2014) 

"If some day we build machine brains that surpass human brains in general intelligence, then this new superintelligence could become very powerful. And, as the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species would depend on the actions of the machine superintelligence." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"Improvements in rationality and intelligence will tend to improve an agent’s decision-making, rendering the agent more likely to achieve its final goals. One would therefore expect cognitive enhancement to emerge as an instrumental goal for a wide variety of intelligent agents. For similar reasons, agents will tend to instrumentally value many kinds of information." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014) 

"Once machines attain some form of human-equivalence in general reasoning ability, how long will it then be before they attain radical superintelligence? Will this be a slow, gradual, protracted transition? Or will it be sudden, explosive?" (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"Some paths to superintelligence require great resources and are therefore likely to be the preserve of large well-funded projects. Whole brain emulation, for instance, requires many different kinds of expertise and lots of equipment. Biological intelligence enhancements and brain–computer interfaces would also have a large scale factor: while a small biotech firm might invent one or two drugs, achieving superintelligence along one of these paths (if doable at all) would likely require many inventions and many tests, and therefore the backing of an industrial sector or a well-funded national program. Achieving collective superintelligence by making organizations and networks more efficient requires even more extensive input, involving much of the world economy." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"Suppose that a digital superintelligent agent came into being, and that for some reason it wanted to take control of the world: would it be able to do so?" (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014) 

"The fact that there are many paths that lead to superintelligence should increase our confidence that we will eventually get there. If one path turns out to be blocked, we can still progress." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"Various considerations thus point to an increased likelihood that a future power with superintelligence that obtained a sufficiently large strategic advantage would actually use it to form a singleton. The desirability of such an outcome depends, of course, on the nature of the singleton that would be created and also on what the future of intelligent life would look like in alternative multipolar scenarios." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"We can tentatively define a superintelligence as any intellect that greatly exceeds the cog‐ nitive performance of humans in virtually all domains of interest." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"We have seen that a superintelligence could have a great ability to shape the future according to its goals. But what will its goals be? What is the relation between intelligence and motivation in an artificial agent?" (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

"[...] we use the term 'superintelligence' to refer to intellects that greatly outperform the best current human minds across many very general cognitive domains. This is still quite vague. Different kinds of system with rather disparate performance attributes could qualify as superintelligences under this definition. To advance the analysis, it is helpful to disaggregate this simple notion of superintelligence by distinguishing different bundles of intellectual super-capabilities. There are many ways in which such decomposition could be done. Here we will differentiate between three forms: speed superintelligence, collective superintelligence, and quality superintelligence." (Nick Bostrom, "Superintelligence: Paths, Dangers, Strategies", 2014)

🖍️Yves Hilpisch - Collected Quotes

"Algorithms without data are often worthless. Similarly, algorithms with 'standard' data from typical data sources, such as exchanges or data service providers like Refinitiv or Bloomberg, might only be of limited value. This is due to the fact that such data is intensively analyzed by many, if not all, relevant players in the market, making it hard or even impossible to identify alpha-generating opportunities or similar competitive advantage." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"All in all, it seems questionable whether a superintelligence can be properly and systematically controlled when it has reached that level. After all, its superpowers can at least in principle be used to overcome any human-designed control mechanism." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"Although the normality assumption is a good approximation for many real-world phenomena, such as in physics, it is not appropriate and can even be dangerous when it comes to financial returns." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"Almost no financial return sample data set passes statistical normality tests. Beyond the fact that it has proven useful in other domains, a major reason why this assumption is found in so many financial models is that it leads to elegant and relatively simple mathematical models, calculations, and proofs." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"Even if markets are weakly efficient on an end-of-day basis, they can nevertheless be weakly inefficient intraday. Such statistical ineffi‐ ciencies might result from temporary imbalances, buy or sell pres‐ sures, market overreactions, technically driven buy or sell orders, and so on. The central question is whether such statistical ineffi‐ ciencies, once discovered, can be exploited profitably via specific trading strategies." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"Even strong proponents of a utopian future based on advancements in AI must agree that a dystopian future after a technological singularity cannot be fully excluded. Since the consequences might be catastrophic, dystopian outcomes must play a role in broader discussions about AI and superintelligence." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"In the definition of learning through an algorithm or computer program, it is important to note the difference between the task of learning and the tasks to be learned. Learning means to learn how to (best) execute a certain task, such as estimation or classification." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"It is to be assumed that any form of superintelligence will have instrumental goals that are independent of its main goal. This might lead to a number of unintended consequences, such as the insatiable quest to acquire ever more resources with any means that seem promising." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"Of all the possible paths to superintelligence, AI seems to be the most promising one. Recent successes in the field based on reinforcement learning and neural networks have led to another AI spring, after a number of AI winters. Many even now believe that a superintelligence might not be as far away as we thought even a few years ago. The field currently is characterized by much faster advancements than originally predicted by experts only a short while ago." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"The first and second moment of a probability distribution only describe a normal distribution completely. There are infinitely many other distributions that might share the first two moments with a normal distribution while being completely different." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"The randomized population of training, validation, and test data sets is a common and useful technique for data sets that are neither sequence-like nor temporal in nature. However, when one is dealing, say, with a financial time series, shuffling the data is generally to be avoided because it breaks up temporal structures and sneaks foresight bias into the process by using, for example, later samples for training and implementing the testing on earlier samples." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

"Whereas in supervised learning the training, validation, and test data sets are assumed to exist before the training begins, in RL the agent generates its data itself by interacting with the environment. In many contexts, such as in games, this is a huge simplification. Consider the game of chess: instead of loading thousands of histor‐ical human-played chess games into a computer, an RL agent can generate thousands or millions of games itself by playing against another chess engine or another version of itself, for instance." (Yves Hilpisch, "Artificial Intelligence in Finance A Python-Based Guide", 2021)

 

🤖〽️Prompt Engineering: Copilot Unabridged (Part 216: How Long Can Microsoft and Other Vendors Sustain Massive AI Investments Before It Becomes an Economic Bubble?)

Prompt Engineering Series

Prompt: "write a post of 600 words on the impact of consistent and high‑quality training data on AI"

Introduction

Over the past several years, Microsoft, Google, Amazon, Meta, and other technology giants have invested unprecedented sums into Artificial Intelligence (AI). They are building GPU superclusters, expanding datacenter capacity, training frontier‑scale models, and subsidizing AI usage across consumer and enterprise products. These investments are justified by the belief that AI will become the next foundational computing platform - driving productivity, reshaping cloud economics, and unlocking new trillion‑dollar markets.

But massive investment comes with massive risk. If AI adoption, monetization, and real‑world impact fail to keep pace with spending, the industry could find itself in a classic economic bubble: inflated expectations, unsustainable burn rates, and a painful correction. The key question is how long vendors can sustain this trajectory before the imbalance becomes too large to ignore.

1. Financial Strength Buys Time - But Not Unlimited Time

Microsoft, Google, and Amazon have enormous financial buffers. Microsoft alone generates more than $80 billion in annual operating income, giving it the ability to absorb AI losses for several years. This financial resilience allows vendors to continue investing even when short‑term returns are modest.

However, financial strength is not infinite. If AI revenue fails to scale, vendors will eventually face pressure to reduce capital expenditure. The sustainability window is long - 3 to 7 years - but not indefinite. This is the core of financial runway.

2. Investor Expectations Are the Real Timer

Investors currently tolerate massive AI losses because they believe in long‑term returns. As long as vendors show:

• rapid adoption
• credible monetization pathways
• strong ecosystem growth
• increasing enterprise integration
• the market remains patient. 

But if expectations diverge too far from reality, investor sentiment can shift quickly.

A bubble forms when expectations grow faster than fundamentals. If AI revenue plateaus while spending accelerates, investors will demand:

• reduced spending
• clearer profitability timelines
• more conservative guidance

This is the dynamic of expectation inflation.

3. Infrastructure Expansion Has Natural Limits

Even if vendors wanted to sustain massive spending indefinitely, physical constraints prevent it. Datacenters require land, power, cooling, and specialized hardware. Supply chains for GPUs and networking fabric are already strained.

These constraints slow the pace of expansion and act as a natural brake on bubble formation. Vendors cannot overspend infinitely because the infrastructure simply cannot scale infinitely. This is the logic behind infrastructure bottlenecks.

4. The Bubble Threshold: When Costs Outrun Value

An economic bubble emerges when the perceived future value of AI becomes disconnected from its actual economic output. Warning signs include:

• AI revenue growing slower than AI costs
• enterprises reducing or delaying adoption
• vendors subsidizing usage at unsustainable levels
• datacenter expansion outpacing utilization
• investors questioning long‑term profitability

If these trends intensify, the bubble becomes visible. Most analysts believe the industry has 3–5 years before this risk becomes acute.

5. What Happens If the Bubble Pops?

If AI fails to meet expectations, vendors would be forced to:

• cut capital expenditure
• slow frontier‑model training
• consolidate datacenter expansion
• shift focus to smaller, more efficient models
• prioritize profitable cloud workloads

The industry would not collapse - but it would undergo a painful correction.

Conclusion

Microsoft and other vendors can sustain massive AI investments for several years thanks to strong balance sheets, strategic necessity, and investor patience. But if AI fails to deliver the expected economic transformation, the industry risks drifting into an economic bubble where spending outpaces value creation.

The sustainability window is long - but not limitless. Without measurable returns, vendors will eventually face pressure to reduce spending, recalibrate expectations, and shift toward more efficient AI strategies. The next few years will determine whether AI becomes the next great computing platform - or the next great over‑investment cycle.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖Prompt Engineering: Transformers (Just the Quotes)

"Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations.  End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks. To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution." (Ashish Vaswani et al, "Attention Is All You Need", 2017) [source] 

"Attention is a mechanism used in deep learning models (not just Transformers) that assigns different weights to different parts of the input, allowing the model to prioritize and emphasize the most important information while performing tasks like translation or summarization. Essentially, attention allows a model to 'focus' on different parts of the input dynamically, leading to improved performance and more accurate results. Before the popularization of attention, most neural networks processed all inputs equally and the models relied on a fixed representation of the input to make predictions. Modern LLMs that rely on attention can dynamically focus on different parts of input sequences, allowing them to weigh the importance of each part in making predictions." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Large language models (LLMs) are AI models that are usually (but not necessarily) derived from the Transformer architecture and are designed to understand and generate human language, code, and much more. These models are trained on vast amounts of text data, allowing them to capture the complexities and nuances of human language. LLMs can perform a wide range of language-related tasks, from simple text classification to text generation, with high accuracy, fluency, and style." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024) 

"[...] LLMs are pre-trained on large corpora and sometimes fine-tuned on smaller datasets for specific tasks. Recall that one of the factors behind the Transformer’s effectiveness as a language model is that it is highly parallelizable, allowing for faster training and efficient processing of text. What really sets the Transformer apart from other deep learning architectures is its ability to capture long-range dependencies and relationships between tokens using attention. In other words, attention is a crucial component of Transformer-based LLMs, and it enables them to effectively retain information between training loops and tasks (i.e., transfer learning), while being able to process lengthy swatches of text with ease." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"This ability to zero in on important code is why modern AI coding assistants can offer meaningful suggestions for your specific needs. It’s similar to how skilled developers know which code sections affect a new implementation the most. Each transformer layer learns about various code patterns, ranging from syntax validation to understanding the relationships among functions, classes, and modules." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"Transformers are complex models built like LEGO blocks using multiple smart and specialized components. [...] Briefly, a vanilla transformer model consists of separate stacks of encoders and decoders. Each encoder block includes multi-head self-attention, enabling the model to capture relationships between tokens regardless of their positions. Residual connections help maintain gradient flow, preventing the vanishing gradient problem. Layer normalization ensures training stability, and feed-forward layers introduce non-linearity and learn complex token interactions. Decoder blocks contain the same components but also include an encoder-decoder attention mechanism to incorporate context from the encoder. The model uses embedding layers to convert tokens into a continuous latent space for contextual learning and positional encoding to preserve the order of tokens in the sequence." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"A transformer is a type of deep learning model designed to handle sequential data, such as text, more efficiently than previous RNNs. It uses a self-attention mechanism introduced [...] to process all parts of a sequence simultaneously, rather than one by one. This allows it to capture relationships between words (or data points) more effectively. The development of transformers revolutionised natural language processing and was a key leap forward. Transformers also used an encoder–decoder architecture. The encoder transforms the input into an abstract representation, and the decoder generates the output. So, by tokenising text into subword units and using parallel self-attention, it enables far greater throughput than RNNs. This design eliminates the need for recurrence (in RNNs) and achieves state-of-the-art results in tasks like translation and allows for a 1,000× speed-up using GPUs." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)

"GenAI encompasses a variety of model architectures, including diffusion models, normalizing flows, and autoregressive models. However, three types of GenAI have become particularly dominant in practical applications. GANs pit two neural networks against each other to produce realistic data. VAEs encode input data into a latent space and decode it to generate new samples. Transformer-based models, like those in the GPT, Llama, and Gemini families, leverage attention mechanisms to generate coherent and contextually relevant sequences." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"Transformers dominate language for several reasons: their autoregressive framework provides a straightforward training objective (predict next token), self-attention captures long-range dependencies across thousands of tokens, parallel training processes entire sequences simultaneously (unlike RNNs’ sequential bottleneck), and scaling behavior produces emergent capabilities at billions of parameters. These properties make transformers uniquely suited for the language understanding and generation tasks central to enterprise AI applications [...]" (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"Transformer-based models represent the dominant architecture for processing sequential data, particularly text. Unlike GANs and VAEs, which excel at generating visual content, transformers have revolutionized natural language processing (NLP) and are now being applied to code, music, and even protein structures. At their core, transformers use a mechanism called self-attention that allows the model to consider relationships between all elements in a sequence simultaneously. This enables transformers to capture long-range dependencies and contextual nuances that earlier sequential models struggled with." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026) 

"Unlike earlier neural network designs that processed text sequentially, transformers use a mechanism called self-attention that allows the model to consider relationships between all words in a passage simultaneously. This parallel processing enables transformers to capture long-range dependencies and contextual nuances that previous architectures struggled with. Positional encoding provides information about word order, while the attention mechanism allows the model to focus on relevant context when processing each word." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 215: What Happens If AI Investments Don’t Meet Expectations?)

Prompt Engineering Series

Prompt: "write a post of 600 words and explain what will happen if the AI investments made by Microsoft and other vendors don't meet the expectations"

Introduction

Over the past five years, Microsoft, Google, Amazon, Meta, and other tech giants have poured unprecedented amounts of capital into Artificial Intelligence (AI) . They have built GPU superclusters, expanded datacenter footprints, acquired strategic partners, and trained frontier‑scale models that cost hundreds of millions per run. These investments are justified by the belief that AI will become the next foundational computing platform - bigger than cloud, bigger than mobile, and potentially bigger than the internet itself.

But what if this future doesn’t materialize as expected? What if adoption slows, monetization lags, or the technology fails to deliver the transformative productivity gains vendors promise? The consequences would be profound, reshaping corporate strategy, investor sentiment, and the trajectory of the entire industry.

1. Financial Pressure Would Force a Strategic Reset

If AI revenues fail to scale, the first impact would be financial compression. AI infrastructure is extraordinarily expensive, and vendors currently tolerate losses because they expect future dominance. Without that payoff, companies would be forced to:

• Reduce capital expenditure on datacenters
• Slow GPU procurement
• Consolidate or cancel frontier‑model training cycles
• Shift investment back toward profitable core businesses

This is the classic pattern of strategic retrenchment - a pivot from aggressive expansion to defensive cost control.

2. Cloud Growth Would Stall

AI is the engine driving the next wave of cloud adoption. If AI underperforms, cloud hyperscalers would lose a major growth vector. Azure, AWS, and Google Cloud rely on AI workloads to justify new datacenter regions and premium compute tiers.

A slowdown would mean:

• Lower utilization of new datacenters
• Reduced demand for high‑margin GPU instances
• Pressure on cloud revenue forecasts

This would be especially painful for Microsoft, whose AI strategy is tightly integrated with Azure’s long‑term growth.

3. Investor Confidence Would Erode

Right now, investors tolerate massive AI losses because they believe in long‑term returns. If expectations are not met, that tolerance evaporates. The market would demand:

• Clear profitability timelines
• Reduced spending
• More conservative guidance
• A shift toward predictable revenue streams

This is the dynamic of market correction - a recalibration of expectations that forces vendors to justify every dollar spent.

4. Competitive Dynamics Would Shift

If frontier‑scale AI fails to deliver, the competitive landscape changes dramatically. Companies that bet heavily on large models would lose strategic leverage, while those investing in smaller, specialized, or hybrid approaches could gain ground.

Meta’s open‑source strategy, for example, would look far more attractive if massive proprietary models fail to produce returns. Smaller vendors could also rise by focusing on niche, cost‑efficient AI rather than universal intelligence.

This is the logic behind competitive inversion.

5. Enterprise Adoption Would Slow

Enterprises currently experiment with AI because vendors subsidize usage. If investments fail, subsidies shrink, prices rise, and adoption slows. Companies would revert to:

• Traditional automation
• Classical machine learning
• Rule‑based systems
• Domain‑specific analytics

AI would remain useful - but not transformative.

Conclusion

If AI investments fail to meet expectations, the consequences would be severe but not catastrophic. Vendors would survive, but the industry would undergo a major strategic reset. Spending would decline, cloud growth would slow, investor pressure would intensify, and the competitive landscape would shift toward smaller, more efficient models.

AI would still matter - but it would no longer be the defining platform of the next era. The companies that adapt fastest to this reality would shape the future, whether or not frontier‑scale AI delivers on its promise

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 214: How Long Can Microsoft and Other Vendors Sustain Massive AI Losses?)

 

Prompt Engineering Series

Prompt: "write a post of 600 words and explain how long the AI investments can be sustained by Microsoft and other vendors as they generate huge losses"

Introduction

The Artificial Intelligence (AI) boom has triggered one of the largest capital outlays in the history of technology. Microsoft, Google, Amazon, Meta, and others are spending tens of billions of dollars annually on GPUs, data centers, research partnerships, and frontier‑model development. These investments generate enormous short‑term losses, raising a critical question: How long can this level of spending be sustained?

The answer depends on three forces: financial capacity, strategic necessity, and market expectations. Together, they determine how long vendors can tolerate losses before AI must begin paying for itself.

1. Financial Capacity: The Balance Sheet Determines the Burn Rate

Microsoft, Google, and Amazon are not startups - they are trillion‑dollar companies with deep cash reserves, diversified revenue streams, and high creditworthiness. This gives them the ability to sustain losses for years, not months.

Microsoft alone generates more than $80 billion in annual operating income, which acts as a buffer for AI losses. As long as core businesses - cloud, enterprise software, Windows, Office - continue to perform, Microsoft can redirect profits to subsidize AI expansion.

This is why financial resilience is the first determinant of sustainability.

2. Strategic Necessity: AI Is Not Optional

AI is the next computing platform. Vendors know that whoever controls the dominant AI ecosystem will shape:

• cloud workloads
• enterprise automation
• developer tooling
• search and advertising
• productivity software

This creates a strategic imperative: spend now or become irrelevant later.

Microsoft’s partnership with OpenAI is not just an investment - it is a defensive moat against Google’s Gemini, Amazon’s Anthropic partnership, and Meta’s open‑source strategy.

This is the logic behind strategic dependency.

3. Market Expectations: Investors Tolerate Losses - For Now

Investors understand that frontier AI is a long‑term play. As long as vendors demonstrate:

• rapid adoption
• strong ecosystem growth
• credible monetization pathways
• increasing enterprise integration
• the market will tolerate losses.

But this tolerance is not infinite. If revenue growth stalls or adoption plateaus, investor pressure will force vendors to slow spending.

This is the dynamic of market tolerance.

4. The Real Constraint: Infrastructure Saturation

The biggest limiting factor is not money - it is physical infrastructure.

Datacenters take years to build. Power grids must be upgraded. Supply chains for GPUs and networking fabric are constrained.

Even if vendors wanted to double spending, they often cannot.

This natural bottleneck slows the burn rate and extends sustainability.

This is the core of infrastructure saturation.

5. When Does the Spending Plateau?

Most analysts expect the current hyper‑investment phase to last 3–5 more years, followed by a stabilization period where:

• model training becomes more efficient
• inference costs decline
• monetization improves
• enterprise AI revenue grows
• datacenter expansion reaches maturity

At that point, losses shrink and AI becomes a net contributor rather than a drain.

Conclusion

Microsoft and other vendors can sustain massive AI losses for several years because they have the financial strength, strategic motivation, and investor support to do so. But this spending cannot continue indefinitely. Physical infrastructure limits, competitive pressure, and the need for profitability will eventually force a shift from expansion to optimization.

AI is following the same pattern as cloud computing: a decade of heavy losses, followed by decades of dominance. The companies investing today are not trying to win the next quarter - they are trying to win the next era of computing.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 213: Why Massive AI Investments Generate Massive Losses)

Prompt Engineering Series

Prompt: "write a post of 600 words and explain why the AI investments made by Microsoft and other vendors generate huge losses,. "

Introduction

Artificial Intelligence (AI) has become the defining battleground of modern technology. Microsoft, Google, Amazon, Meta, and others are pouring tens of billions into AI infrastructure, model training, and ecosystem development. Yet despite explosive public interest and rapid enterprise adoption, these companies report staggering short‑term losses tied directly to their AI initiatives.

This paradox - sky‑high investment, sky‑high losses-is not a sign of failure. It is a structural feature of frontier‑scale AI. To understand why, we need to examine the economics behind training large models, the infrastructure required to run them, and the strategic pressures that force vendors to spend aggressively even when profitability is years away.

1. Frontier‑Model Training Costs Are Exponential

Training a frontier model is not a linear expense. Each generation requires more parameters, more training tokens, larger datasets, and more compute cycles. A single training run for a cutting‑edge model can cost hundreds of millions of dollars.

This is why frontier‑model training is the first and most visible driver of losses. Vendors must run multiple training cycles, safety evaluations, fine‑tuning passes, and inference optimizations. Microsoft’s partnership with OpenAI means Azure absorbs the bulk of these compute costs, directly impacting earnings.

2. Infrastructure Build‑Out Is Historically Unprecedented

AI does not run on ordinary cloud servers. Vendors must build:

• GPU superclusters
• High‑bandwidth networking fabrics
• Liquid‑cooling systems
• Specialized datacenters optimized for AI workloads

Each hyperscale datacenter costs $1–$2 billion, and hardware depreciates quickly. Today’s top‑tier GPU becomes mid‑tier in 18–24 months. This creates a cycle of continuous capital expenditure that depresses short‑term profitability.

This is the core of AI infrastructure economics.

3. Inference Costs Scale With Usage

Traditional software has near‑zero marginal cost. AI does not.

Every query to a large model consumes compute, electricity, and cooling. When millions of users interact with Copilot, ChatGPT, Gemini, or Claude, vendors pay for every token generated.

This is why AI inference is a structural loss generator: revenue must grow faster than usage to break even, which rarely happens in early adoption phases.

4. Monetization Is Still Immature

Most users expect AI to be:

• Free
• Unlimited
• Always available

But the cost structure makes that impossible. Vendors experiment with subscriptions, API pricing, enterprise licensing, and usage‑based billing, yet none of these models currently offset the full cost of running frontier AI.

This is the challenge of AI monetization.

5. Competition Forces Overspending

AI is an arms race. No vendor can afford to fall behind. This creates irrational spending patterns:

• Microsoft invests heavily to stay ahead with OpenAI
• Google accelerates Gemini development
• Amazon pours billions into Anthropic
• Meta open‑sources massive models to shape the ecosystem

In an arms race, losses are tolerated because the alternative is losing strategic control of the next computing platform. This is the logic behind competitive overspending.

Conclusion

AI investments generate huge losses because vendors are not selling a finished product—they are building the foundation of a new computing era. Frontier‑scale AI requires unprecedented capital, massive compute, and continuous reinvestment. The losses are not a sign of weakness; they are the cost of securing future dominance in a market that will reshape productivity, cloud infrastructure, search, advertising, and enterprise automation

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🏭𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐚𝐛𝐫𝐢𝐜: 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 (𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰)

 Introduction (Some Background)

Typically, PowerPoint slides and similar content must be broken in small pieces and structured in such a way that the audience can digest the information presented. Thus, a presentation ends up spreading over multiple slides that must be structured in such a way that it facilitates also digestion, retention, and whatever further aspects are targeted. Conversely, on social networks the average author/publisher has only a small chance of capturing an audience's attention, given that the competition for readers' attention increases exponentially in the long sequence of posts. So, what information would we show for this purpose? 

When I want to learn something, typically I need a few pieces of information/knowledge that would allow me to anchor and integrate the text into existing knowledge. It usually starts with high-level definitions of the main concepts that reflect the various aspects that would help me associate and differentiate the respective concepts from similar concepts or knowledge. The volume of such information depends on many aspects - the complexity of the concepts and the contexts they belong to, how many similar concepts or metaphors are available, the gaps available, analogies and differentiators, etc.

A high-level list of the main capabilities for the respective concept(s) would further allow a (wider) surface to anchor the various information of interest. No matter how much text can be included, we shouldn't forget that a (well-chosen) picture is (often) worth a thousand words. Therefore, a well-chosen image that depicts a high-level representation of the concepts/ideas presented, the architecture or even a metaphor can have a considerable impact on the readers.  

Ideally the image should serve as a sketch or sample of the finished product. Then, even if from the used representation one can guess what are the ingredients used, the difference between a good dish and a fiasco often resides in details. Like in preparing a dish, one needs to know what ingredients are needed and how they must be prepared and used together, for the maximum effect. An experienced cook needs at least the list of ingredients and some general information on how the dish differentiates from other dishes. The more inexperienced the cook, the more information needs to be provided and somewhere a line must be drawn, otherwise a simple recipe becomes the cookbook itself. Therefore, like in any cookbook, I must assume that some basic knowledge is available!

So, as part of the learning/teaching process, I started a series of slides on Microsoft Fabric that should help me narrow down the recipes for delivering great products. The information comes from training material, various presentations delivered by Microsoft or third-parties, the various books and other technical material I read over the years. 

Let's start with the basics - the (data) warehouse!

   📣Technical Overview 

    

Conclusion

The slide barely scratches the surface, especially if we consider it from a broader context - what the average professional must know. Even if the gap between the presented information and a business case, for example, or anything similar capable of convincing the audience is considerable, the slide should serve as a starting point, if not a foundation on which something durable can be built. 

Enjoy the ride and feel free to like it, download it, respectively share it further!

🖍️Hayden Van Der Post - Collected Quotes

"A critical aspect of neural networks is their ability to learn from data. This learning occurs during the training phase, where the network is exposed to vast datasets, allowing it to adjust its internal parameters - the weights and biases associated with each neuron. The goal of this adjustment is to minimize the difference between the network's predictions and the actual outcomes, a process known as optimization. Through techniques such as gradient descent and backpropagation, neural networks iteratively refine their parameters, enhancing their ability to make accurate predictions or decisions based on new input."(Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Beyond the choice of model, the accuracy of neural network predictions in trading significantly depends on the quality and relevance of the data fed into them. This underscores the importance of meticulous data preparation, encompassing cleaning, normalization, and feature engineering. By ensuring that the input data is reflective of the market's complexities, traders can fine-tune their neural networks to produce more accurate and actionable predictions." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Deep learning algorithms are exceptionally adept at identifying anomalies within large datasets, making them an indispensable tool for detecting fraudulent transactions and financial irregularities. By learning from historical transaction data, these models can pinpoint patterns and behaviors indicative of fraudulent activities with remarkable accuracy. This ability not only aids in safeguarding assets but also ensures compliance with increasingly stringent regulatory standards aimed at preventing financial fraud and misconduct." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Deep learning is an advanced subset of machine learning, distinguished by its ability to process data through layers of neural networks, each layer abstracting information from the one preceding it. This hierarchical approach enables the model to handle complex, high-dimensional data, learning features and patterns at multiple levels of abstraction. [...] Traditional neural networks, with their shallower architectures, often struggle with the nuances of financial data, limited by their capacity to extrapolate and interpret intricate patterns. Deep learning, however, with its deeper, more sophisticated networks, can navigate these complexities, offering nuanced insights into market dynamics." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Feature selection is the first critical step in model optimization. It's the art of identifying which features in your dataset contribute most significantly to the outcome you're trying to predict. This not only helps in enhancing the model's accuracy but also in reducing computational complexity, leading to more efficient models. [...] While feature selection is about cherry-picking the most useful features, feature engineering is about creating new features that increase the predictive strength of the model. This is where creativity and domain knowledge come into play, especially in financial data, where market sentiment, economic indicators, and other external factors can influence market movements." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Genetic programming represents a frontier in feature engineering, allowing for the automated creation of new features through the application of evolutionary algorithms. By combining existing features in non-linear and complex ways, genetic programming can uncover hidden relationships in the data that were not apparent through manual exploration. This technique, while computationally intensive, holds the promise of discovering novel predictors that can enhance the performance of trading algorithms." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Neural networks are structured into layers, each comprising a collection of neurons. The arrangement begins with an input layer, which receives the raw data. This is followed by one or more hidden layers, where the actual processing happens through a complex web of interconnected neurons. The journey through the layers culminates in an output layer, where the network delivers its final decision or prediction. The hidden layers are the cradle of the network’s learning capability, enabling it to detect patterns, make associations, and refine its predictions through repeated exposure to data." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Predictive analytics, involves the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Neural networks, with their remarkable ability to learn and model complex patterns, have become the backbone of modern forecasting methods. Their application ranges from predicting consumer behavior in retail to forecasting the stock market trends, from anticipating weather patterns to foreseeing potential healthcare outbreaks." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"The beauty of neural networks lies in their ability to learn and improve. Through a process known as 'training', a neural network is fed large amounts of data along with feedback on its performance. This feedback guides the network in adjusting its internal parameters, known as weights, to minimize errors in its predictions. This iterative process of learning from mistakes closely mirrors the cognitive and learning processes of the human brain, making neural networks particularly adept at tasks that involve pattern recognition, such as image and speech recognition [...]" (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"The essence of backpropagation is captured in the gradient descent algorithm, which seeks to minimize the error by iteratively adjusting the weights in the direction that most steeply decreases the error function. This rigorous process requires a meticulous balance; too large a weight adjustment can lead to erratic learning, while too small an adjustment can trap the network in local minima. In algorithmic trading, the capacity to learn from past predictions and refine strategies accordingly is invaluable, allowing for the continual optimization of trading algorithms in alignment with market dynamics." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"The power of neural networks lies in their flexibility and adaptability. They are not confined to a single type of problem or dataset but can be tailored to a wide range of applications, from voice recognition and image classification to forecasting financial market movements. This versatility stems from the network's ability to capture and model complex, non-linear relationships within the data it is trained on, making it a potent tool in the arsenal of data scientists and algorithmic traders alike." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"Weights and biases are pivotal in shaping the neural network's decision-making process. Weights determine the strength of the connection between two neurons, influencing how much of the input signal is passed forward. Biases, added to the weighted sum before the activation function, allow neurons to adjust their output independently of their input, providing an additional degree of freedom. The process of learning in a neural network involves adjusting these weights and biases based on the error between the network's predictions and the actual data, typically using an algorithm like gradient descent." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)

"While forward propagation provides the prediction, backpropagation is the mechanism through which a neural network learns from its errors and enhances its accuracy. Backpropagation, a form of reverse engineering of the forward propagation process, involves calculating the error between the predicted output and the actual output, and then distributing this error back through the network. This distribution occurs layer by layer, in reverse order from output to input, adjusting the weights of the connections based on the magnitude of the error." (Hayden Van Der Post, "Neural Network: Mastering the Art of Algorithmic Trading", 2024)