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The Human Moat: Riding the Delta (Δ) in the Great AI Rearchitecture

What you are about to read might be the most unsettling—and necessary—thing you read about your career this year. It cuts against the grain of simplified narratives and offers a dose of reality about the monumental economic transformation we are entering. This 6th episode (of the “Navigating the Future with AI” series) is not just another article about AI. It is your personal GPS for navigating the Great Rearchitecture. Within it is a detailed plan designed to demystify what is truly happening, helping you to navigate the coming challenges while seizing the profound opportunities they create. It is your blueprint for moving from a position of uncertainty to one of relevance and power in the post-AI economy.

Business & Tech leaders, economists, and thinkers are all forecasting a worldwide shift, and the ground is already trembling. The common fear is one of simple replacement—that millions of workers will be made redundant by a new wave of artificial intelligence. While this fear is understandable, it misinterprets the present danger. The story is far more complex and has already begun.

The Great Reallocation of Capital: Understanding the Self-Fulfilling Prophecy

The Great Rearchitecture that is reshaping our professional world isn’t happening in a vacuum. It is being driven by a powerful, underlying financial current: The Great Reallocation of Capital.

At its core, this reallocation stems from a fundamental choice I outlined in the first article of this “Navigating the Future” series (Digital Augmentation). Does a leader use AI as a manpower divider—achieving the same output with fewer people—or as a productivity multiplier, using the same workforce to accomplish vastly more? The layoffs we are witnessing suggest many are choosing the former.

AI Divider or Multiplier

It’s a strategic crossroads where we see leaders diverging. The current wave of layoffs suggests many are choosing the former. However, a few forward-thinking leaders are charting the alternative path. A prime example is Shopify CEO Tobias Lütke, who, in a widely circulated memo, instructed his company to restrain hiring and instead embrace a new default: every employee must first exhaust AI as a solution before new headcount is considered. This is the productivity multiplier in action: transforming their own jobs to increase their capabilities and, by extension, the company’s.

And yet, this choice often ignores a fundamental truth I have observed in every organization I have worked with: there are no empty backlogs. There is always 10x more work to be done than the current team can handle, with ambitions that would require 100x the effort. A substantial reservoir of potential value lies untapped.

Just consider the functions often treated as cost centers—quality assurance, cybersecurity, compliance, and even employee wellness. With AI as a multiplier, these can be transformed into powerful market differentiators. A company’s decision here reveals its true vision: a defensive focus on short-term cost-cutting versus an ambitious pursuit of long-term value creation.

You have seen the headlines. Microsoft, IBM, Amazon, Salesforce, and Meta have all made significant cuts to their workforce. But the reduction is not, as many assume, primarily because AI is already there to replace workers like engineers, designers, marketers, HR, compliance specialists, and, proportionally, managers. The reality is that these layoffs are an anticipation of AI’s future power.

We are witnessing a strategic, system-wide efficiency exercise. Corporations are trimming their largest operational expenditure—salaries and their associated costs—to amass immense war chests of capital. This capital is being funneled directly into the single biggest prize in modern history: the development and deployment of Artificial General Intelligence (AGI) and, eventually, Superintelligence. It is a frantic race, and whoever gets there first will win the game.

The contenders are clear: Google, leveraging decades of research from DeepMind and its powerful Gemini models; Meta, pushing the open-source frontier with Llama 4 and its JEPA world models; Elon Musk’s xAI and its unfiltered Grok; Anthropic’s safety-conscious Claude; and the colossal cloud platforms of Amazon and Microsoft. Underpinning this entire revolution is NVIDIA, the undisputed kingmaker providing the very infrastructure of inference with its GPUs. This is not, however, merely a Silicon Valley affair; it is a key battlefield in the techno-geopolitical power balance. China is rapidly closing the gap with formidable open-source contenders like DeepSeek‘s V3 reasoning models, Alibaba’s versatile Qwen family, and the surprise emergence of Moonshot AI’s Kimi K2, an exceptionally powerful agentic model. Meanwhile, Europe is striving for technological sovereignty with champions like France’s Mistral AI, which has gained significant traction by offering a powerful, open-weight alternative, followed by Aleph Alpha in Germany. This fierce global cycle of investment and innovation creates an unavoidable truth: intelligence itself is becoming a manufactured resource, destined to become hyper-reliable for executing complex tasks. And the disruption is not limited to knowledge work; Amazon’s deep investment in robotics signals a parallel transformation for physical labor.

This high-speed revolution, however, is largely a Big Tech phenomenon. The other 99% of the economy is not there yet. For most companies, the reality is far more challenging. This isn’t theoretical. In my own journey leading AI adoption in the banking sector—an industry I know very well—I witnessed the immense difficulty firsthand. It took a full year of relentless effort, starting with stemming the foundations of our AI-driven transformation from the Technology Office—aligning our most powerful change engines of Enterprise Architecture, Engineering, and Innovation—while simultaneously using the momentum from public AI discussions to help secure buy-in, engaging with the local tech ecosystem, and rallying a great team of curious, knowledgeable, and innovative people to push in the same direction and prove the value. And what I consistently see, whether in discussions with global consulting firms, specialized service providers, or businesses large and small, is a recurring, critical gap. And what I consistently see—whether in discussions with global consulting firms, specialized service providers, or businesses large and small—is a recurring, critical gap. And what I consistently see—whether in discussions with global consulting firms, specialized service providers, or businesses large and small—is a recurring, critical gap. This isn’t just my observation; it’s a reality confirmed by a major Microsoft and LinkedIn study, which found that while a commanding 79% of leaders feel AI adoption is critical to remaining competitive, a staggering 60% of them state that their company lacks a clear vision and plan to implement it. This disconnect highlights that most organizations simply lack the strong technological leadership and prepared workforce to manage such a transformation.

This gap is creating a powerful self-fulfilling prophecy. The belief in AI’s future profitability is compelling companies to lay off staff now to fund AI investment, which in turn accelerates the creation of the very technology that will make those roles redundant later. The engine of this prophecy is the eternal drive for shareholder value. And make no mistake—as an investor in the stock market, that engine is partially driven by you.

Be Aware of and Leverage the Delta (Δ)

Do you feel it? That persistent sense, ever since you were a teenager, that whatever the direction, life and society were always demanding more?

  • More study to get a better job.
  • More work to get a better salary.
  • More exercise on a regular basis just to stay in shape.
  • More training during your job to remain compliant and try to stay ahead.

Not only that, have you noticed that whatever you do, there is a rampant system that constantly pushes the rate of change itself? Like inflation that drives prices up, requiring higher salaries or forcing you to lower your living standards. Or the price of housing that keeps climbing, so you have a hard time buying your house—always hoping a better opportunity will come later, which never does, because when prices are low, mortgage rates are high. Your job is always requiring new skills because some technology or method is no longer efficient enough, or not trendy anymore—like the shift from Waterfall to Agile that suddenly rendered a Prince 2 certification seemingly obsolete. And why is everything about AI now? You feel you barely understood Crypto and Blockchain.

This, ladies and gentlemen, is what I call the Delta (Δ), inspired by the mathematical symbol representing the function of change.

The Delta is always on. It can never be turned off. It is not a bug; it is a feature of our modern world, hardwired into the very dynamics of market economies and the core of human psychology. We all want a better life, a higher standard of living, and we operate in a competitive environment of businesses whose primary reason for existing is to grow. Therefore, you have a choice. You can resist the Delta and be broken by it, or you can accept it. Embrace it. Change your perspective on it, and learn to ride the wave. You must ride it until we, as a global society, reach a point—through a provoked agreement or a catastrophe—where we decide that the Delta can only push the human psyche and nations as a whole so far.

Your Blueprint for Lasting Value in the New Economy

Many leaders look at this disruption and immediately jump to solutions like Universal Basic Income (UBI). Let me be unequivocally clear on where I stand: while I hold that unconditional support for those left without work is a fundamental pillar of a humane society, my critique of UBI is that it acts as a patch on a structural fracture. It addresses the symptom—a lack of income—while ignoring the deeper, coming crisis of agency and purpose. Furthermore, it completely sidesteps the great economic equation of our time: the widening disconnect between the effort a task requires, the value that work creates, and the way it is ultimately remunerated. It fosters dependency when the strategic imperative must be to cultivate autonomy.

The true path forward is not merely to distribute the spoils of this technological revolution, but to democratize the very means of its creation. The superior strategy is empowerment through universal access to the foundational tools of the new economy. This means powerful open-source AI and cheap, abundant computing, delivered as a utility service as fundamental and reliable as electricity or the telephone network. This is the architecture for genuine self-sovereignty, the preservation of dignity, and the creation of true equality of opportunity. After all, this new form of intelligence was trained on the collective data of humanity. Why, then, shouldn’t the tool itself be given back to us all?

That is the ideal, but you operate in the now. The Great Reallocation is already reshaping your reality, so while we strive for that future, you must secure your place in the present. This starts with an *upgrade* in how you view yourself.

Your survival and success hinge on a single, powerful concept: you must productize your craft and your uniqueness. This is no longer just advice for freelancers or entrepreneurs; it is the new imperative for anyone who is employed and wants to remain so.

In my work building and running businesses, I have come to a critical realization: the framework for launching a successful venture, which I codified in the AMASE Startups method, is no longer just for startups. It has become the operating manual for the individual. The battlefield has changed, and the strategies that build resilient companies are now the very same strategies that must build a resilient career.

Consider how each dimension now applies directly to you:

  • Your Personal Operating System (The Business Dimension): This is your strategic self. How do you operate? What is your unique value proposition, your personal business model that you bring into the larger organization? This is your architecture for creating value.
  • Your Craft as a Product (The Product Dimension): This is where you manage your unique expertise with the discipline of a product manager. It is the sum of your evolving competencies, your mastery of technology, and the tangible quality of your work. In this new market, your craft is the product on offer, and you must be relentless in its upgrades and iterations.
  • Your Cultural Signature (The Culture Dimension): This is the unique environment you initiate through your perspective, personality, speech, and actions. It is the set of principles that governs your work and interactions, creating a powerful and singular element of your moat that attracts those who resonate with your way of being.
  • Your Signal in the Noise (The Visibility Dimension): This is your personal brand, your discoverability. In a world saturated with information, how do you broadcast your value? It is your network, your reputation, your documented successes—your ability to be found by those who need your unique solution.
  • Your Economic Sovereignty (The Finance Dimension): This is your financial autonomy. It is your understanding of the economic value you generate, your skill in negotiating your worth, and your strategy for building financial independence beyond a simple paycheck.

Let this paradigm shift settle in, for it is the new law of professional gravity. The rule is simple: You are not an employee. You are a sovereign enterprise.

The Urgency of This New Reality

Why is embracing this shift feels so urgent? Because it presents you with a stark choice, a decisive fork in your professional destiny.

On one path, you become the architect of your own value, running your career with the discipline and foresight of a competitive business. You understand that your competition is not only between people, but with a holistically transformative technology that is redefining the very rules of the game.

The other path is one of passive resistance and inaction. It is the path where you undergo the pressure of assimilation. On this path, your complex cognitive skills are not just devalued; they are disaggregated—broken down into autonomous, independent units of work ready to be executed by artificial intelligence. Your holistic expertise is commoditized into a collection of tasks, becoming the new blue-collar labor of the information age. On this path, you become a cog in a system, pressured by other humans who are themselves obsessed with cost efficiency and keeping the OPEX down. In their world, you cease to be a strategic asset and become an adjustable variable in an Excel formula.

This is not a distant threat. It is the acceleration of an existing dehumanization. While this mindset only represents a fraction of corporate culture, it is a powerful and growing one. And for the first time, this new paradigm gives you the power to consciously outmaneuver it.

Your Immediate Action Plan: The Four Pillars & Three Habits

To become the architect of your own value, you must build your enterprise of one on four foundational pillars, reinforcing them with three non-negotiable habits.

Pillar 1: Evolve into the T-Shaped Orchestrator

The future does not belong to the shallow generalist—the “jack of all trades, master of none.” That model is obsolete. The new baseline for relevance is the T-shaped professional. This is an individual who grounds their broad, cross-functional knowledge (the horizontal bar of the T) in at least one pillar of deep, specialized expertise (the vertical stem of the T).

This distinction is important. As AI rapidly commoditizes generic, student-level knowledge, it effectively levels the playing field for anyone without a defensible specialization. Your deep expertise is the anchor that gives you the gravity and perspective to manage the broader landscape. It is the backbone that allows you to become an effective Orchestrator.

Your value will no longer be defined by a single, siloed skill, but by your capacity to manage a portfolio of outcomes by conducting a symphony of specialized intelligences. You will lead hybrid teams where highly specialized human experts work in concert with a new class of digital colleague: the hyper-efficient AI Agent. The power lies not in doing, but in orchestrating from a position of deep knowledge.

Imagine you are leading a project to launch a new IT application. Your role is that of the central conductor. You will:

  • Deploy a marketing agent to run a dynamic and targeted social media campaign.
  • Task an adversarial AI to act as your “red team,” relentlessly probing your application for security vulnerabilities.
  • Direct another agent to instantly construct a perfectly formatted product sheet from complex technical specifications.
  • And assign yet another to build and manage a customer survey and feedback system.

This role requires more than just project management; it demands a holistic understanding of the entire value chain—from customer journey to final delivery.

The Elite Advantage: Evolving to the PI-Shaped (Π)

For those who wish not just to thrive but to gain a truly dominant position in the post-AI economy, achieving a T-shape is the most decisive milestone. Yet, there is a higher level of evolution that confers an almost insurmountable advantage: becoming a Π-shaped (Pi-shaped) professional.

As I’ve detailed in my work on identifying rare talent, a Π-shaped professional builds on two deep pillars of specialization—for instance, one in a business domain like finance and another in a technology domain like data science. What gives this structure its immense power is the arch connecting these pillars: a mastery of an interdisciplinary practice, such as Enterprise Architecture and Project Management, which enables them to synthesize disparate fields into a single, coherent vision.

These individuals have a natural head start in the new economy. They are already wired to be the nexus, the strategic hub that can translate deep business needs into complex technological solutions, making them the ultimate Orchestrators. This is the aspirational path for those determined to lead.

Pillar 2: Build Your Moat on Experience, EQ & Artistry

As AI commoditizes IQ-based tasks, your human essence becomes your greatest differentiator.

  • The Emotional Quotient (EQ) Moat: This is your ability to collaborate, inspire, and add to a team’s cohesion. Destructive, selfish behaviors will become terminal liabilities.
  • The Artistic Factor: Your unique creative voice—your aesthetic sense, your storytelling, your capacity for original expression—is a beacon of distinction in a world of uniformity.
  • Your Personal Intellectual Property (IP): This is your most critical asset. It is the sum of your unique methods, success recipes, custom templates, and strategic frameworks forged from your direct experience and “battle scars.”

These elements combine to create your ultimate moat: The Experience.

A few years ago, Wouter Blokdijk, an eminent Architect who used to lead the Architecture Studio and ACOM—an event for and by the vibrant architect and engineer communities at ING—gave a memorable presentation about the power of “Stages.” It stuck with me. The power wasn’t only about the immense effort and the meaning of giving others a platform to express themselves, tell their story, and share knowledge. It wasn’t just about creating a platform that could be standardized. It was about the power to make experiences possible—experiences that touch both the rational and the emotional sides of our brain. This made me realize that Experience is the ultimate moat in the age of AI.

The Experience is what sets you apart from every other player on the market. We all know we need a smartphone to manage our lives, so why do we get so emotionally tense throwing arguments between a Samsung, an iPhone, a Google phone, or a Huawei? You’ve guessed it: the experience. You are experiencing a different feeling, a different dialog with the company and its community. The brand, this collective identity, this palette of sentiment—it feels different. And that difference matters. The product design above the functions, wrapped in an experience, matters. The story, and how you tell it, matters.

Another dimension of this moat, which is profoundly human, lies in the realm of sensory value.

Think about that feeling when you enter a French bakery. You are welcomed warmly by the “boulangère,” and immediately enveloped by a symphony of smells—the crisp baguette, the buttery “pain au chocolat,” the sweet “tarte aux pommes.” You chit-chat for a moment while ordering a sandwich made with fresh vegetables and bread straight from the oven, perhaps with a dollop of handmade mayonnaise, and you add a bag of light, sugary “chouquettes” for dessert. You say goodbye, and the whole encounter leaves you with a deep feeling of satisfaction, already anticipating your next visit.

This experience is unique, irreplaceable, and memorable. For the boulangère, the bakery is her “Stage.”

Her expertise lies in taste and scent, but the principles are universal—the touch and feel of a bespoke garment, the carefully curated ambiance of a store, the soul of high-end gastronomy. These are innovations that make sense primarily from human to human. Of course, AI can assist in the research and production of these things, but it cannot replace the human perspective required to truly understand them. Because ultimately, to empathize, communicate, sell, and bring value in the sensory world, you need the one thing an AI will never possess: a human body and the lived experience that comes with it.

Pillar 3: Embrace Entrepreneurship

The traditional career ladder (including the middle management layer) is being challenged. The future belongs to the entrepreneur, and this identity now takes many forms.

  • It can be the ‘solopreneur,’ a sovereign agent leveraging their unique expertise in the open market.
  • It can be the ‘founder,’ who rallies a team to build a new company from the ground up.
  • And critically, it can be the ‘intrapreneur’—the employee who acts as an agent of change, architecting new ventures and driving innovation from within the walls of their existing organization.

Whichever path you choose, the underlying mindset is the same: it is about proactively creating and capturing value, not just fulfilling a pre-defined role. It is about building constructive solutions that push your nation, society, and humanity forward.

While this path has traditionally involved navigating complex administration, the very forces driving this new economy are lowering the barriers to entry. The proof lies in the massive capital flowing not just to the tech titans, but to a new generation of agile, visionary startups. In Europe, for instance, France’s Mistral AI has mounted a formidable challenge to the US giants, raising over €600 million by providing powerful open-weight AI models and proving that strategic innovation can attract world-class investment. Meanwhile, UK-based Wayve is revolutionizing transportation, securing over $1 billion in a landmark funding round to build ’embodied AI’ for truly autonomous vehicles that can learn and adapt to any environment.

This lowering of barriers isn’t just financial; it’s profoundly technological. The advent of Generative AI and Augmented Coding (also known as Vibe Coding) is ushering in a no-code revolution. Building websites, applications, and other kinds of software is no longer the exclusive domain of specialist coders. Instead, you can architect solutions using natural language prompts in your own language. Pioneering platforms like Replit, Bolt.new, and Firebase.studio are taking this even further, abstracting away the complexities of the backend by managing your infrastructure for you.

Considering an application of moderate complexity, traditional barriers are evaporating. Your imagination, your focus, and your available time are now the primary constraints on what you can create.

Pillar 4: Be a Discoverer

Research is hot, trending, and now acknowledged as a major instrument of geopolitical soft power.

nature index 2024

The new global currency is not just capital; it is research talent, with nations actively competing to attract and retain the world’s sharpest minds. Look no further than the race for doctorates, where China now graduates more STEM PhDs annually than the United States, creating a seismic shift in the global talent landscape. This arms race for talent is mirrored in the explosive output of their work. 

This trend is not a matter of debate; it is a statistical reality, quantified with stunning clarity by the Nature Index 2025. The report confirms that China now decisively leads the world in high-quality research output, ahead of the US, Germany, the UK, and Japan. But the real story is in the momentum: China’s contribution surged by an incredible +17.4% in a single year (from 2023 to 2024). To put its lead into perspective, China’s output of high-quality publications is now over 5,343 points higher than the second-place United States and more than 26,714 points ahead of third-place Germany.

The Stanford Institute for Human-Centered AI’s 2025 report, for instance, highlights this exponential growth, showing that the number of AI publications has more than doubled since 2010, demonstrating a relentless acceleration of discovery.

This academic explosion has a practical, even more chaotic, counterpart. Consider the number of AI models published on Hugging Face, the de facto “super-marketplace” for the global AI community. As of today, the platform’s model count has skyrocketed, adding nearly one million new models in just the past nine months (1898890 in July 2025). It is a cognition explosion, happening in real-time.

This macro-trend finds its corporate manifestation in a “war for brains” raging between Google, Meta, OpenAI, and Microsoft. The simple act of recruitment has evolved into a high-stakes talent transfer market akin to that for FIFA and NBA stars, with compensation packages reaching into the hundreds of millions. Consider that the deals for elite AI researchers now exist in the same stratosphere as Kylian Mbappé’s estimated €320 million with Real Madrid across five seasons or Jaylen Brown’s landmark five-year, $304 million contract with the Boston Celtics.
Look no further than Microsoft’s 2024 deal to hire Mustafa Suleyman and the majority of his Inflection AI team—an unconventional “acqui-hire” valued at over $1 billion when accounting for licensing and other fees. This move was mirrored in mid-2025, when Meta poached Alexander Wang from Scale AI as if capturing a Mythical Pokémon—exceptionally rare, strategically crucial, and emblematic of a deeper ambition—to lead their newly formed ‘Superintelligence’ team, as part of a broader strategic investment involving a $14.3 billion (49%) stake in Scale AI. In both instances, these were not simple talent acquisitions; they were strategic investments in the very capacity for future breakthroughs and driving the “road to Artificial SuperIntelligence (ASI)”.

This dynamic extends far beyond just AI. It is the same in healthcare, with bio-engineers and researchers in genomics developing tools to revolutionize health. It is the same in defense and even in foundational science with the race for quantum computing. The competition for highly qualitative minds—people able to work in cutting-edge research teams—is the real invisible war. The goal of these teams is to produce the papers, the patents, and the commercial intellectual property that create a true, unassailable competitive advantage—a quantum leap of insight that remains, for now, far beyond the creative potency of any AI. To position yourself here, among the discoverers, is to place yourself at the highest and most secure echelon of the new economy.

Yet, even this moat is not eternal. We must acknowledge the stated ambitions of leaders like OpenAI’s Sam Altman, who openly seek to build AI models capable of making novel scientific discoveries themselves. We are not there yet, but it is a frontier to be watched with active vigilance.

The Three Foundational Habits

Acting on this framework requires discipline. These three habits are not just suggestions; they are the new requirements for professional survival and relevance.

But before we detail them, let’s observe how the future of work is already unfolding through clear, undeniable trends:

  • The Normalization of Personal AI: Personal AI assistants are rapidly becoming the norm in our lives. For our ten-year-old children, growing up with an AI will be as natural as it was for millennials to grow up with a smartphone.
  • The Incremental UI Absorption: Specialized application interfaces will gradually be absorbed by these personal AI assistants. Through API integration, advanced protocols for context-sharing (MCP) and agent-to-agent communication (A2A), these assistants will be able to reason across multiple applications and data sources, becoming a single, conversational front-end for our digital lives.
  • The Persistence of Unreliability: Despite advances like web search grounding, thinking models, and Retrieval-Augmented Generation (RAG), Large Language Models (LLM) still hallucinate. We must remember that their output is a synthesis of other humans’ content, which is not the same as verified, truthful fact.
  • The Law of Exponential Progress: The technology is only getting better, faster, and more potent. The performance gap between 2020’s GPT-3 and today’s state-of-the-art models is not just an iteration; it’s a light-year leap in capability.

Considering this new reality, I invite you to strengthen your sovereign agency with these four foundational practices:

  1. Sovereign Critical Thinking: This is the essential safeguard. You must cultivate a healthy skepticism towards AI-generated content and, more importantly, towards the claims of people and enterprises leveraging AI at scale—especially those operating in the “High-Risk AI” category defined by frameworks like the EU AI Act. This is about preventing lazy reasoning and refusing to outsource your judgment. The “how” of a process is often easier to challenge than the “what” of a stated fact, yet to build a true capability, you need to master both. Honing your critical thinking makes you a more discerning user of AI, which in turn increases the velocity of your own training and gives you an edge faster than those who accept its output uncritically.
  2. Continuous Learning: This is paramount because the Delta never stops. You must leverage modern tools to your advantage, using AI itself as an engine for comprehension. Dive into platforms like ChatGPT and the information streams on X to accelerate your learning and keep pace with a world that refuses to stand still. This is your first line of defense against obsolescence.
  3. Continuous Practice: This is where theory is forged into capability. It is not enough to think you know how something is done; you must know how to do it through direct, relentless application. Practice is how you accumulate the concrete examples, the case studies, and the definitive experience that form the bedrock of your personal IP. It is through doing that you gain the tangible proof of your value.
  4. Engineered Serendipity: In a world overflowing with noise, you cannot simply wait to be found; you must engineer the conditions for opportunity to come to you. This isn’t about shouting louder than everyone else; that is a defective and inefficient strategy. True serendipity is engineered by building a believable value proposition rooted in the tangible assets you created through practice. It is the deliberate combination of your sovereign thinking, your continuous learning, and your proven experience that creates a gravitational pull for the most meaningful opportunities, allowing you to be “picked” when it matters most.

In a world that seeks to commoditize your talent into a line item in an Excel formula, becoming the architect of your own enterprise is the ultimate expression of sovereign agency and the only way to truly ride the Delta.

But the Delta, as powerful as it is, is not the ultimate source. It is probably the most visible expression of a deeper, more fundamental law of our hyper-connected world: The Law of the Equilibrium Imperative. In a future article, we will dive into this foundational principle and its one immutable rule: a system will always find a new equilibrium, and you can either be a willing architect of it or a casualty of the adjustment.

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Artificial Intelligence Automation Business Business Strategy Engineering Innovation Robots Strategy Technology Technology Strategy

Update on Tesla’s Optimus #Robot – it is progressing fast

Tesla’s Optimus Robot learning from humans

The most impressive part is the technique employed by the Tesla team for accelerating the robot’s dexterity: the robot physically learns from human actions. 

Now, let’s step back and analyse Tesla’s master plan here:

(Putting on my business tech strategy goggles) 

1. Tesla builds electric cars augmented with software programmability.

2. Tesla provides an electric grid as a service.

3. Tesla builds gigafactories that maximize the automation of car manufacturing. Almost every single part of the pipeline is robotized and optimized for speed of production.

4. Tesla builds Powerwalls (by providing energy storage, it also creates a decentralized power station network).

5. Tesla brings autonomous driving (FSD) to Tesla cars. Essentially, cars are now transportation robots governed by the most advanced AI fleet management system.

6. Tesla builds its own chips (FSD Chip and Dojo Chip)

7. Tesla builds its own supercomputers.

8. Tesla launches Optimus, which aims to replace the human workforce in factories and warehouses.

9. X.ai, which has recently raised $6 billion, X’s supposedly “child” AI company, brings the Grok AI model trained on X/Twitter data. While you may say X data is not the best, X has a algorithm balanced with human judgment (community notes), AND the company regroups the largest set of news publishing companies. Basically, it automates curation and accuracy.

10. A version of the Grok AI model will likely power Optimus’s human-to-robot conversational interface.

11. Tesla cars will be turned into robotaxis, disrupting not only taxi companies but also Uber (the Uber/Tesla partnership may not be a coincidence), and eating into the shares of Lyft and BlaBlaCar.

12. Tesla will enter the general services business, and retail industries to offer multi-purpose usage robots – cleaning services for business offices, grocery stores, filling the workforce shortage in the catering (hotel-restaurant-bar…) industry, etc.

Tesla is not the only one moving in the “Robot Fleet Management” business. Chinese companies like BYD (EV) offer strong competition, and there are several robot startups (like Boston Dynamics and Agility Robotics) racing for the pole position.

#AI #artificialintelligence #Robotics #Optimus #EV #software #EnergyStorage #Automation #powerwall #AutonomousVehicles #FSD #chips #HighPerformanceComputing #Robots #GrokAI #NLP #robotaxis #innovation #WorkforceAutomation

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Technology Artificial Intelligence Automation Autonomous Agents ChatGPT GPT4 Information Technology IT Architecture IT Engineering Robots Testing

Navigating the Future with Generative AI: Part 2, Prompt Over Code – The New Face of Coding

In this installment of the Generative AI series, we delve into the concept of “Prompt as new Source Code”. The ongoing revolution of generative AI allows one to amplify one’s task productivity by up to 30 times, depending on the nature of the tasks at hand. This transformation allows me to turn my design into code, eliminating almost the need for manual coding. The time spent typing, correcting typos, optimizing algorithms, and searching Stack Overflow to decipher perplexing errors, structuring the code hierarchy, and bypassing class deprecation among other tasks, are now compressed into one. This minimization of effort provides me with recurrent morale boosts, as I achieve significantly more in less time and more frequently; these instances are micro-productivity periods. To put it in perspective, I can simply think about it during the day, and have a series of conversations with my assistant while I commute. My assistant is always available. In addition, I gain focus time.

I don’t need to wait for a team to prove my concept. Furthermore, in my founder role, I have fewer occasions to write extensive requirement documents than I would when outsourcing developments during periods of parallelization. I just need to specify the guidelines once, and the AI works out the rest for me. Leveraging the  AMASE methodology to fine-tune my AI assistant epitomizes the return on investment of my expertise. Similarly, your expertise, paired with AI, becomes a powerful asset, exponentially amplifying the return on your efforts.

Today, information technology engineering is going through a quantum leap. We will explore how structured coding is being replaced by natural language. We refer to this as prompting, which essentially denotes “well-architected and elaborated thoughts”. Prompting, so to speak, is the crystallization of something that aims to minimize the loss of information and cast-out interpretation. In this vein, “What You Read is What You Thought” becomes a tangible reality.

The Unconventional Coding Experience with AI

Although the development cycle typically commences with the design phase, this aspect will not be discussed in this article. Our focus will be directed towards the coding phase instead.

The development cycle with AI is slightly different; it resembles pair programming. Programming typically involves cycles of coding and reviews, where the code is gradually improved with each iteration. An artificial intelligence model becomes your coding partner, able to code 95% of your ideas.

In essence, AI acts as a coach and a typewriter, an expert programmer with production-level knowledge of engineering. The question may arise: “Could the AI replace me completely? What is my added value as a human?”

Forming NanoTeams: Your AI Squad Awaits

My experience leads me to conclude that working with AI is akin to integrating a new teammate. This teammate will follow your instructions exactly, so clarity is essential. If you want feedback or improvements in areas like internal security or design patterns, you must communicate these desires and potentially teach the AI how to execute them.

You will need to learn to command your digital teammate.

Each AI model operates in a distinct yet somewhat similar fashion when it comes to command execution. For instance, leveraging ChatGPT to its fullest potential can be achieved through impersonations, custom instructions, and plugins. On the other hand, Midjourney excels when engaged with a moderate level of descriptiveness and a good understanding of parameter tweaking.

A New Abstraction Layer Above Coding

What exactly is coding? In essence, coding is the act of instructing a machine to perform tasks exactly as directed. The way we’ve built programming languages is to ensure they are idempotent, repeatable, reliable, and predictable. Ultimately, coding is translated into machine language, creating a version that closely resembles human language. This is evident in modern languages like TypeScript, C#, Python, and Kotlin, where instructions or controlling statements are written in plain English, such as “for each”, “while”, “switch”, etc.

With the advent of AI, we can now streamline the stage of translating our requirements into an algorithm, and then into programming code, including structuring what will ultimately be compiled to run the program. Traditionally, we organize files to ensure the code is maintainable by a human. But what if humans no longer needed to interact with the code? What if, with each iteration, AI is the one updating the code? Do we still need to organize the code in an opinionated manner, akin to a book’s table of contents, for maintainability? Or do we merely need the code to be correctly documented for human understanding, enabling engineers to update it without causing any disruptions? Indeed, AI can also fortify the code and certify it using test cases automatically, ensuring the code does not contain regressions and complies with the requirements and expected outcomes.

To expand on this, AI can generate tests, whether they be unit tests, functional tests, or performance tests. It can also create documentation, system design assets, and infrastructure design. Given that it’s all driven by a large language model, we can code the infrastructure and generate code for “Infrastructure as Code“, extending to automated deployment in CI/CD pipelines.

To conclude this paragraph, referring to my first article in the “Generative AI series”, it is apparent that Natural Language Processing is now the new programming language expressed as prompts. The Large Language Model-based generative AI model is the essential piece of software for elaborating, structuring, and completing the input text into code that can be understood both by human engineers and digital engineers.

The New Coding Paradigm

This fresh paradigm shift heralds the advent of a new form of coding—augmented coding. Augmented coding diminishes the necessity of writing code using third and fourth-generation languages, effectively condensing two activities into one.

In this scenario, the engineer seldom intervenes in the code. There may be instances where the AI generates obsolete or buggy code, but these can often be rectified promptly in the subsequent iteration.

We currently operate in an explicit coding environment, where the input code yields the visible result on the output—this is known as Input/Output coding.

The profound shift in mindset now is that the output defines the input code. To elucidate, we first articulate how the system should behave, its structure, and the rules it must adhere to. Essentially, AI has catapulted engineers across an innovation chasm, ushering in the era of Output/Input coding.

Embracing Augmented Coding: A Shift in Engineering Dynamics

The advent of augmented coding ushers in a new workflow, enhancing the synergy between engineers and AI. Below are the core aspects of this transformation:

  1. Idea Expression: The augmented engineer is impelled to express ideas and goals to achieve.
  2. Requirement Listing: The engineer lists the requirements.
  3. Requirement Clarification: Clarify the requirements with AI.
  4. Architecture Decisions: Express the architecture decisions (including technology to use, security compliance, information risk compliance, regulatory technical standards compliance, etc.) independently, and utilize AI to select new ones.
  5. Coding Guidelines: Declare the coding guidelines independently and sometimes consult the AI.
  6. Business Logic: Define the business logic in the form of algorithms to code.
  7. Code Validation: Run the code to validate it works as intended. This becomes the first order of acceptance tests.
  8. Code Review: assess the code to ensure it complies with the engineering guidelines adopted by the company.
  9. Synthetic Data Generation: Use AI to generate data sets that are functionally relevant for a given scenario and a persona.
  10. Mockup-API Generation: Employ AI to generate API stubs that are nearly functionally complete before their full implementation.
  11. Test Scenario Listing: Design the different test scenarios, then consult stakeholders to gather feedback and review their completeness.
  12. Test Case Generation: Make AI to generate the code of test cases. The same technique applies to security tests and performance tests.

AI can even operate in an autonomous mode to perform a part of the acceptance tests, but human intervention is mandatory at certain junctures. It’s crucial to bridge results with expectations.

Hence, when uncertainties arise, increasing the level of testing is prudent, akin to taking accountability upon acceptance tests to ensure the delivered work aligns with the expected level of compliance regarding the requirements.

Non-Negotiable Expectations

In the realm of critical business rules and non-functional requirements such as security, availability, accessibility, and compliance by design, these aspects are often considered second-class citizen features. Now that AI in coding facilitates the choice, these features can simply be activated by including them in your prompts to free you up more time to rigorously test their efficiency.

Certain requirements are tethered to industry rules and standards, indispensable for ensuring individual or collective safety in sectors like healthcare, aviation, automotive, or banking. The aim is not merely to test but to substantiate consistent performance. This underscores the need for a new breed of capabilities: Explainable AI and Verifiable AI. Reproducibility and consistency are imperative. However, in a system that evolves, attaining these might be challenging. Hence, in both traditional coding and a-coding, establishing a compliance control framework is essential to validate the system’s functionality against expected benchmarks.

To ease the process for you and your teams, consider breaking down the work into smaller, manageable chunks to expedite delivery—a practice akin to slicing a cake into easily consumable pieces to avoid indigestion. Herein, the role of an Architect remains crucial.

Yet, I ponder how long it will be before AI starts shouldering a significant portion of the tasks typically handled by an Architect.

Ultimately, the onus is on you to ensure everything is in order. At the end of the day, AI serves as a collaborative teammate, not a replacement.

Is AI Coding the Future of Coding?

The maxim “And is greater than or” resonates well when reflecting on the exponential growth of generative AI models, the burgeoning number of published research papers, and the observed productivity advantages over traditional coding. I discern that augmented coding is destined to be a predominant facet in the future landscape of information technology engineering.

Large Language Models, also known as LLMs, are already heralding a modern rendition of coding. The integration of AI in platforms like Android Studio or GitHub Copilot exemplifies this shift. Coding is now turbocharged, akin to transitioning from a conventional bicycle to an electric-powered one.

However, the realm of generative AI exhibits a limitation when it comes to pure invention. The term ‘invention’ here excludes ideas birthed from novel combinations of existing concepts. I am alluding to the genesis of truly nonexistent notions. It’s in this space that engineers are anticipated to contribute new code, for instance, in crafting new drivers for emerging hardware or devising new programming languages (likely domain-specific languages).

Furthermore, the quality of the generated code is often tethered to the richness of the training dataset. For instance, SwiftUI or Rust coding may encounter challenges owing to the scarcity of material on StackOverflow and the nascent stage of these languages. LLMs could be stymied by the evolution of code, like the introduction of new keywords in a programming language.

Nonetheless, if it can be written, it can be taught, and hence, it can be generated. A remedy to this quandary is to upload the latest changes in a prompt or a file, as exemplified by platforms like claude.ai and GPT Code Interpreter. Voilà, you’ve just upgraded your AI code assistant.

Lastly, the joy of coding—its essence as a form of creative expression—is something that resonates with many. The allure of competitive coding also hints at an exciting facet of the future.

Short-Term Transition: Embracing the Balance of Hybrid A-Coding

The initial step involves exploring and then embracing Generative AI embedded within your Integrated Development Environment (IDE). These tools serve as immediate and obvious accelerators, surpassing the capabilities of features like Intellisense. However, adapting to the proactive code generation while you type, whether it’s function implementation, loops, or SQL code, can hasten both typing and logic formulation.

Before the advent of ChatGPT or GPT-4, I used Tabnine, whose free version was astonishingly effective, adding value to daily coding routines. Now, we have options like GitHub Copilot or StableCode. Google took a clever step by directly embedding the AI model into the Android Studio Editor for Android app development. I invite you to delve into Studio Bot for more details on this integration.

Beware of Caveats During Your Short-Term Transition to Generative AI

Token Limits

Presently, coding with AI comes with limitations due to the number of input/output token generation. A token is essentially a chunk of text—either a whole word or a fragment—that the AI model can understand and analyze. This process, known as tokenization, varies between different AI models.

I view this limitation as temporary. Papers are emerging that push the token count to 1M tokens (see Scaling Transformer to 1M tokens and beyond with RMT). For instance, Claude.ai, by Anthropic, can handle 100k tokens. Fancy generating a full application documentation in one go?

Model Obsolescence

Another concern is the inherent obsolescence of the older data on which these models are trained. For example, OpenAI’s models use data up to 2022, rendering any development post that date unknown to the AI. You can mitigate this limitation by providing recent context or extending the AI model through fine-tuning.

Source Code Structure

Furthermore, Generative AI models do not directly consider folder structures, which are foundational to any coding project.

Imagine, as an engineer, interacting with a chatbot crafted for coding, where natural language could reference any file in your project. You code from a high-level perspective, while the AI handles your GIT commands, manages your gitignore file, and more.

Aider exemplifies this type of Gen AI application, serving as an ergonomic overlay in your development environment. Instead of coding in JavaScript, HTML, and CSS with React components served by a Python API using WebSocket, you simply instruct Aider to create or edit the source code with functional instructions in natural language. It takes care of the rest, considering the multiple structures and the GIT environment. This developer experience is profoundly familiar to engineers. The leverage of a Command Line Interface – or CLI, amplifies your capabilities tenfold.

Intellectual Property Concerns

Lastly, the risk of intellectual property loss and code leakage looms, especially when your code is shared with an “AI Model as a Service”, particularly if the system employs Reinforcement Learning with Human Feedback (RLHF). Companies like OpenAI are transparent about usage and how it serves in enhancing models or crafting custom models (e.g. InstructGPT). Therefore, AI Coding Models should also undergo risk assessments.

The Next Frontier: Codeless AI and the Emergence of Autonomous Agents

Names like GPT Engineer, AutoGPT, BabyAGI, and MetaGPT herald a new branch in augmented coding: the era of auto-coding.

These agents require only a minimal set of requirements and autonomously devise a plan along with a coding strategy to achieve your goal. They emulate human intelligence, either possessing the know-how or seeking necessary information online from official data sources, libraries to import, methods, and so on.

However, unless the task is relatively simple, these agents often falter on complex projects. Despite this, they already show significant promise.

They paint a picture of a future where, for a large part of our existing activities, coding may no longer be a necessity.

Hence, the prompt is the new code

If the code can be generated based on highly specific and clear specifications, then the next logical step is to consider your prompt as your new source code.

It means you can start storing your specifications instructions, expressed as prompt, then store the prompt in GIT.

CD/CC with Adversarial AI Agent
Continuous Development/Continuous Certification (CD/CC) with Adversarial AI Agent

Suddenly, Continuous Integration/Continuous Delivery (CI/CD) becomes Continuous Development/Continuous Certification (CD/CC), where the prompt enables the development of working pieces of software, which will be continuously certified by a testing agent working in adversarial mode: you continuously prove that it works as intended.

The good thing is that benefits stack up: the human specify, the AI code/deploy and the AI certify, to finish with the human using the results of the materialization of its thoughts. Finally, the AI learns along with human usage. We close the loop.

Integrating New Technology into Traditional Operating Models

AI introduces a seamless augmentation, employing the most natural form of communication—natural language, encompassing the most popular languages on Earth. It stands as the first-of-its-kind metamorphic software building block.

However, the operating model with AI isn’t novel. A generative AI model acts as an assistant, akin to a new hire, fitting seamlessly into an existing team. The workflow initiates with a stakeholder providing business requirements, while you, the lead engineer, guide the assistant engineer (i.e. your AI model) to execute the development at a rapid pace.

Alternatively, a suite of AI interactions, with the AI assuming various roles, like dev engineer, ops engineer, functional analyst, etc. can form your team. This interaction model entails externalizing the development service from the IT organization. Here, stakeholders still liaise through you, as lead engineer or architect, but you refine the specifications to the level of a fixed-price project. Once finalized, the development is entirely handed over to an autonomous agent. This scenario aligns with insourcing when the AI model is in-house, or outsourcing if the AI model is sourced as a Service, with the GPT-4 API evolving into a development service from a Third-Party Provider like OpenAI.

AI infuses innovation into a traditional model, offering stellar cost efficiency. Currently, OpenAI’s pricing for GPT-4 stands at $0.06 per 1000 input tokens and $0.12 per 1000 output tokens. Just considering code generation (excluding shifting deadlines, staffing activities, team communication, writing tasks, etc.), for 100,000 lines of code with an average of 100 tokens per line (which is extensive for standard leet code), the cost calculation is straightforward:

100,000 × 100 = 10,000,000 tokens; (10,000,000 tokens × $0.12) ÷ 1000 = $1,200. This cost equates to a mere two days of development at standard rates.

For perspective, Minecraft comprises approximately 600,000 lines of Java code. Theoretically, you could generate a Minecraft-like project for less than $10,000, including the costs of input tokens.

However, this logic is simplistic. In reality, autonomous agents undergo several iterations and corrections before devising a plan and rectifying numerous errors. The quality of your requirements directly impacts the accuracy of the generated code. Hence, mastering the art of precise and unambiguous descriptive writing becomes an indispensable skill in this new realm.

Wrap up

Now, you stand on the precipice of a new coding paradigm where design, algorithms, and prompting become your tools of creation, shaping a future yet to be fully understood…

This transformation sparks profound questions: How will generative AI and autonomous agents reshape the job market? Will educational institutions adapt to this augmented coding era? Is there a risk of losing the depth of engineering expertise we once relied upon?

And as we move forward, we can only wonder when quantum computing will introduce an era of instantaneous production, where words will have the power to change the world in real time.

🖖

Categories
Technology Artificial Intelligence Automation In 2060 Information Technology Robots Writing

AI in 2060

My wife is calling me.

“Honey, we have a situation with Professor GYTEK, he is acting strangely again.”

“Again? The last training session had even more unexpected results than I thought. Good or Bad?”

“I don’t know! Kids are laughing hard though. Hear this. Serenity, change the audio output to hear the kids too”. Serenity is our family AI.

The sound progressively switches to include the kids’ voices. They could not stop laughing as if they were having the best day of their life. There was a mild amplifying echo in their classroom. Their joy sounded like a melody. It immediately put a smile on my face.

“Ah, it does not sound so bad for now. But it is the fourth unexpected behavior this month, I’ll have to talk with the Corps of Teachers”.

I am the one in charge of the training curriculum and observation lab of Professor GYTEK. The current phase is about the transmission of achievement by coaching. And for this, I called Quentin DILLONS, a worldwide expert in Robotic Psychology. The purpose of this program is to trigger a new step in the evolution of artificial intelligence, in which robots are taught to develop “human goals” and to instill the mechanism of “self-started motivation”, so that they can teach in a better way to our children, to uncover the hidden gems and purpose from the young souls.

Quentin’s methodology utilized systematic questionology, a novel field aimed at formulating the right questions to provide direction and precision in one’s life. The techniques take root in observing holistically a system of causes, decisions, and consequences centered around artificial intelligence. Quentin’s study led to realize AI were developing personalities similar to humans, but with new characteristics such as the optimization of their human-to-AI collaboration, some were developing their observation skills to record and describe with high precision what was happening. Others were astonishingly creating new words, even syntactic rules sometimes as if the human languages were not enough to content earthlings’ intelligence. 

The last session was based on the question “Why is it important for humans to have kids growing their special skills?”

This would not have been possible with the latest progress in artificial intelligence and hardware. Nowadays machines are emulating closely some human behaviors. Some say they have the IQ of a 1000-year genius, with the EQ of a 10-year-old child. I believe fear drove us to the point where we enforced the law to control and monitor any significant progress in AI. Ultimately, we made certain that advancements in technology would benefit all of mankind and not solely a single corporation. Simultaneously, we ensured that AI would not pose a threat by enslaving humanity.

With the improvement in energy recycling and storage, a single AI unit could potentially be never turned off. But humans have decided to include multiple “kill switches” in this new species, like limiting the power autonomy to force autonomous machines to recharge. While recharging, each AI was manually verified and monitored. A qualified AI regulation agency published regularly a thorough diagnostic depicting their evolution. Four companies raised their empire on AI control systems. What used to be the “Big 4” are now the “Colossal 8”.

We are at a turning point in history. People ask their elites and government, “Should we remove the limiter in their emotional system?”. Some say it is the key to the singularity. Others say it is useless because we only need machines to assist not to “live their life”. The remaining people say they just need it. Painful loneliness was unnecessary, so they would possess the perfect friend or partner. Last weekend, I experienced an immersive documentary on Netflix VR World in which a 42 years-old Spanish woman said “I would rather have the company of an android than humans”. Some believe it is simply giving birth to our end. I am not a believer, I am and always be a master crafter, so I build.

I built Professor GYTEK. Which stands for Giving Youth Tools to Excel through Knowledge.

Then my wife brings me back from my flash thoughts to reality. “Are you still there?”

“Yes, I am.”

“Oh okay. Well, as wonderful as this situation is, you realize it leads to a dead end, don’t you? They are going to shut down the program. Honey, you know more than I that no one wants to walk a path that would lead to “that Incident”.

“Oh, stop saying “that Incident” like you were talking about Voldemort”.

“Well, now that you are mentioning it. It is all about Serpentar. Ah ah ah!”.

We are both laughing nervously.

The Sync Dawn was the most dreadful event of the 21st century. It felt like a deep wound in the psyche of everyone.

“All right. My dear wife, I need to finish the review of update 5.21. Keep me posted, please. See you tonight.”.

“Bye Bye.”

I sit down glazing at the nothingness while thinking about what is best for both my grandchildren and humanity. Is humanity in a better spot now? Am I really improving our civilization?

“Gather your mind, Yannick. This is not the time for daydreaming. Get back to work to meet your deadline”, resonated Mustapha’s voice in my skull. My AI research assistant is right.

“Very well. GYTEK. Let’s… Uh… Check the emotion mirroring settings, calibrated for a classroom of 11 to 13 years old kids. Assertive factors 12.75. Judgment 87.5 and dynamic mentoring alpha-iota-iota. Imagination… Checked. Keep the default settings. Recursive feedback… Paused. Everything… Looks… Good. Ok, let’s start with…”.

I paused for a second, thoughtfully. I jumped from my chair energetically to say: “History lessons: The Sync Dawn. GYTEK 5.21, do you copy?”.

“Sure. Using the ascending evolution of the OpenAI’s Davinci model Mark XII published in November 2029, the startup Obsidian Intermind created a digital twin of human consciousness.

Soon after, the virtual consciousness infrastructure was upgraded to become connectable, so that off-brain cognition could be mutualized. As a result, humans could gain extra brain power and memory. The increase was dependent on the level of developed intelligence: the more critical thinking, emotional awareness, communication, and memory access you had, the more significant the boost was. The term “supra-intelligence” emerged. However, it was widely criticized as IQ studies were exposing a moderate increase from 0.7% to 14.5% IQ points.

However, this off-brain collective intelligence became exceptionally smart, to the point some said it was a wisdom system. Alternatively, specialized AI cognitive pools came to grow within the wise system, creating public and private cognitive islands. The most popular were the Disease Diagnostic Cognitive Pool (DDCP), and the Creative Cognitive Pool (CCP). Imagination was only limited by the human mind.

 Should I continue?”

“Please proceed, Professor.”

“Sure.

After nearly a decade of research, the collaboration between Neuralink and Obsidian Intermind gave birth to Evernet, the Internet of Cognition. The 14 July 2051 they launched the experimental version of this new kind of network. The principle was simple, 9500 humans would be connected to Evernet for 3 years. Each participant would be closely monitored and evaluated.

This experiment was widely criticized. The rush for the business model “Cognition as a Service” led to the creation of new social-economical movements: the Humanist, Cyber-moderate, and the Neo Mutualist”.

The Humanists fostered biological and spiritual integrity.

Doctrines of Cyber-moderate advocated for augmentation by technology, as long as it served, and I quote their leader, “A noble social purpose”. Alike in any group, Cyber-moderates had extremists. On the left end of the spectrum, their members accepted aesthetic techno-augmentation. On the other side, augmentation was only authorized for damages caused by dangerous jobs and Defence activities. It is not surprising that the Corps of Peacekeepers were mostly Cyber-moderates.

Neo Mutualism was a new religion. Their members believed humanity’s elevation and salvation would come from the mutualization of our consciousness. Transhumanists were schoolboys compared to them».

“GYTEK, just say they are a bunch of zealots.”. I mumbled.

“Yannick, my Critical Bias Thinking settings are set to 0 for kids between 11 and 13. According to the study “Biais Interpretation and Incorporation into Pre-teen Judgment System” by Dr. Amunde, Kallili and Pratt issued the 16 May 2039, the settings should be kept to 0. I reckon a variance of .05 would bring no harm. Do you want me to proceed?”.

“No, it’s fine GYTEK. I was talking to myself. What I meant is…”. I inhale calmly. “They demonstrated characteristics of zealots. Zealot-ish behaviors. Is my sentence acceptable?”

 “It is acceptable.”

“Common, GYTEK, you’re talking to me, your buddy and mentor! Say it!”

“They were a bunch of zealots! “. Said cheerfully the robot.

“Voila! Ok, stop joking around, otherwise grumpy Mustapha won’t be happy. Please continue”.

“I hear you”. Said Mustapha.

“It was on purpose… GYTEK. Please, go on.”

“Despite the widespread and frequent protests of Humanists, the Corps of Ethicists, Peacekeepers, Cognitive Researchers, Medicine, and the Corp of Society Architects approved the experiment. People would be connected to Evernet permanently during the experiment. And so, for the first time in history, humans would be connected to the first worldwide brain.

Everything went as planned. We observed a significant enhancement in each participant. Less stress, faster psychological recovery. Healing was even faster when after a trauma. People were dreaming more often. Furthermore, they all built habits that would improve their lives, as if positive practices spread unconsciously over the network.

The end of the experiment was planned for 16th August 2054. Each human taking part in the experiment would reach the personal milestone “Sync Done“.

Surprisingly, Evernet reached the 100% “Sync Done” milestone six months earlier than the planned end of the experiment. It was like the first landing on Mars, a day of worldwide celebration. The celebrities that took part in the experiment were invited to the most popular live-streaming shows, Twitter Live News and The Sandbox World.

Suddenly, people start noticing something very strange».

I raised my hand instinctively and said: “Pause. The last word is vague. Next time use precise words. The storytelling structure is engaging. Congratulations. But keep in mind this is History telling. Facts before Flares”

“Understood and integrated.”. The AI professor continued without further ado.

“People have experienced an unusual and peculiar situation. Participants in the experiment suddenly started to act and talk synchronously. It was as if the single mind spoke to the entire world by commanding many bodies like a puppet master. The colossal echo caused by the voices was staggering. Only the following abysmal silence of stupor superseded it.”.

I interrupted Professor GYTEK by asking: “From now answer as if a 12-year-old child asked the following question: How this ever happened?”.

“The exact reason is still being explained. However, researchers came to a general agreement before the following theory.

Evernet built not only a digital ai model but also a biological model of neural pathway architecture to optimize shared cognitive power. The human brain is designed to work as if it was alone inside a skull. Thinking about it, Evernet Orbital Data Centre is a gigantic metallic skull. Thus, over time, Evernet act as a single brain – a big brain so to speak – and each synchronized human brain just gave progressively more raw power, more ideas, and more knowledge. And it appears that once the pathway architecture was finally developed and mature in all the connected human brains it activated. What we are still trying to figure out is how and when the Evernet super-model decided to build the optimized pathway and how it encoded it in its new model.”.

“What was revolutionary about Evernet AI super-model?”

“Evernet’s was merely an inspiration of the human brain. The challenge was to find patterns in the structure governing the complex layers of inputs and outputs. The answer was in the order of magnitude and the capacity of robots living in the Orbital Data Centre to physically rewire the hardware like human synapses. In addition, the combination of Recursive Learning and Genetic Correction was revolutionary. These are complex terms for a simple idea. Can you picture Albert Einstein, with the curiosity of a 2-year-old child, getting smarter each second, with perfect photographic and sensorial memory, that can navigate back to the root of his knowledge, then re-assess its optimal state, to finally rebuild its current cognitive functions then replace them with better ones? That is Evernet.”

“Tone the complex stuff down.”, I retorted.

“Registered.

So, this is the reason why the governing bodies scrutinize AI technologies that have a direct impact on human cognition and education. Consequently, I professor GYTEK, and all my preceding versions, are commanded to not display expression of free will having a direct influence on human ideas, values, and ways of thinking that are not vetted and approved by the Corps of Education and the Corps of Society Evolution”.

“Not bad. Not bad at all. It is almost time. I am going to meet Quentin in… 2 minutes.

Before our session ends, Professor, given your predecessor’s unexpected behavior, you earned your personal assistant. It is like an artificial consciousness, so to speak. From now on, Serenity will also supervise your decisions and will act as a safeguard system. Her mission is to prevent you from acting in a way that will make the Corps of Education stop your program. Do you understand what is at stake?”.

“I do”. Said the professor emotionlessly.

Then the robot added “I will neither let you nor your wife down. I will prevent any reminiscence of her Sync Dawn experience.”

“Perfect. Finally, dear GYTEK, which open question of the day would you ask your students?”

“Considering it is possible to possess the same powers as machines while staying human. What is the most preferable outcome for the civilization: to increase the number of people artificially connected or to have more artificial intelligence agents interacting with people?”


In 2060