Jameel Gordon 11/6/25 Jameel Gordon 11/6/25

The Future Is Here: How AI Will Disrupt Every Industry And What We Can Do

I was recently on a call where a branding company was pitching their rebrand of Oaks + Oars, my regenerative research and strategy studio. It was an excellent presentation with stunning visuals, thoughtful ideas, and a clear explanation of the meaning and depth behind each design element. As someone who has worked in advertising and marketing for nearly two decades, I was genuinely impressed by the work of this independent firm.

But throughout the presentation, I kept wrestling with my own thoughts about my work and my organization. I found myself asking: Is this the type of work we should be doing? I was also captivated by their small team of two principals, it seemed, supported by a network of contractors around the world to give them a global presence. Their work was amazing, and they frequently used the word digital.

At one point, I paused and asked whether the pieces they were showing were mock-ups or actual deployed work. They said mock-ups which, in my mind, made their work even more impressive. When the presentation ended, I asked a simple question: Was any of this AI-generated? They said no, and that’s when I voiced my concerns.

People are not truly aware of, or ready for, what’s happening in the world of artificial intelligence. This is a tool that is not yet fully developed, not yet fully deployed, and whose full potential we do not yet understand and yet, it is already poised to be wildly disruptive.

I gave them an example from one of their own previous clients: Spotify.

With AI, any individual can now code a Spotify clone with a simple voice prompt. What would separate this person’s platform from Spotify or Apple Music? The licensing deals these companies have with recording labels and independent artists. But with another simple prompt, that individual could populate their platform with vast, genre-spanning and genre-bending music almost instantly. Where does this leave the recording industry, its supporting ecosystem, and the artists themselves? There is no way to prevent this level of disruption. Laws could be enacted, but doing so would stifle the very innovation society needs and the opportunity this individual now has.

Here’s the truly unsettling part: what happens to all the wealth created by the perceived value of music catalogs? Quite frankly, much of it is already at risk. Many AI systems have been trained on these catalogs, and new AI models may not even require them. The disruption won’t just impact the recording industry it threatens the intellectual property, the perceived value of that IP, and the wealth built on it.

This scenario extends far beyond music. Every industry including manual labor roles like electricians and plumbers will eventually face disruption. Those jobs may just be last on the pecking order.

I often hear pushback that people simply dislike AI and this level of disruption may not occur. In other words, no one wants to listen to AI generated music. I get it. Fear and skepticism are everywhere. I experience it firsthand as someone who publicly acknowledges that they developed the tool. It’s not fanfare because people are afraid. But that doesn’t change the reality: AI and its infrastructure are advancing rapidly, and its potential impact on society is profound. The consequences will be enormous, and many are not yet prepared.

After the branding pitch, I had to confront the question I’ve been asking myself for months: based on my skills and creations, have I worked myself out of a job? I’ve built a tool that does what I do even though it cannot replicate my unique insight. For instance, I am already thinking ahead to what I call AI 2.0. Yet, economically, I’m still tethered to the present, forced to decide whether I want to act like a snake oil salesman. As I told a trusted futurist and AI ethics colleague, unless you are a cloud-based platform developing and deploying AI, you are essentially selling promises others can now generate as fast as you can conceive them. My value lies in my ability, my privilege, and my foresight because I designed the damn thing and can guide others through positioning themselves beyond disruption. But even that requires extraordinary skill and balance.

Where does this leave us? I believe that, eventually, we will all work for planet Earth. In the meantime, we must focus on what AI cannot do for us. It cannot sleep for us. It cannot eat healthy for us. It cannot exercise for us. These simple, vital acts are essential to preserve our humanity. We must take care of ourselves and each other, because we will need all hands on deck as we continue to roll out this profoundly important and transformative tool for the benefit of all humanity.

I write this as I continue to grieve the passing of my father; a legend in the broadcasting and music industry. 🥹🥹🥹

Jameel Gordon 10/8/25 Jameel Gordon 10/8/25

The Age of Abundance: When AI Eats the Software That Ate the World

I don’t think people fully understand what’s happening right now.

We are not living through another industrial revolution. We are living through the dissolution of everything we thought industry meant. The AI age isn’t a chapter in human progress it’s the prologue to an entirely new civilization.

For the first time in history, we are entering an age of abundance. An era where scarcity itself begins to vanish.

From Scarcity to Superabundance

Every human system from economics to education, from art to architecture has been built on one foundational assumption: scarcity. There is not enough time, not enough money, not enough resources, not enough energy. Entire nations rise and fall on the premise that something, somewhere, must be limited.

Artificial intelligence dismantles that premise.

For centuries, technology has automated production making things faster, cheaper, and more accessible. But AI is doing something radically different. It is automating imagination. It is automating intelligence. And in doing so, it’s dissolving the walls that once separated producers from consumers, thinkers from doers, builders from dreamers.

This is not about replacing jobs. It’s about replacing the very logic of human work itself.

AI doesn’t simply make things — it makes making things obsolete.

The Collapse of the Departmental World

Consider marketing — the department that once connected human desire to human production. Entire industries were built around understanding what people wanted and convincing them to buy it. But when AI understands your preferences better than you do, when it can create and deliver the product instantly, the entire concept of marketing dissolves.

We will not have marketing departments in the world that’s coming. We will have ecosystems that are self-sustaining, self-correcting, self-optimizing flows of information and creation.

The same will happen across every discipline. The borders between business, science, art, and engineering are already blurring into a single continuous intelligence. The organizational chart collapses into a network. And in that network, value doesn’t move in transactions it emerges. Read more here: Human Resources Is No Longer Your Strategy

Automation of Everything

It’s easy to understand AI automating production. Machines can build, design, and distribute. That part is visible.

What’s less visible and far more revolutionary is that AI will also automate consumption and exchange.

Imagine a world where systems understand your patterns of need, preference, and context not as data to exploit, but as information to harmonize. You won’t “buy” things. You’ll simply exist within a flow of fulfillment, where goods and services move through you like air through lungs.

Now extend that one step further. The exchange of value — what we now call “currency” — becomes automated too. Digital currencies, smart contracts, and AI-managed economies will coordinate global trade without human intermediaries. Value will move at the speed of thought.

In that sense, AI is not just automating what we do. It’s automating how we live.

When Software Eats Itself

For decades, Silicon Valley has lived by a mantra: “We build software that eats the world.”

That phrase described how code replaced everything: manufacturing, media, communication, commerce. But what’s happening now is more profound.

Artificial intelligence is eating the software that ate the world.

The tools that once built empires of code are being devoured by systems that learn, adapt, and rewrite themselves. Software was our attempt to capture intelligence in lines of logic. AI is intelligence liberated from logic altogether.

This isn’t just evolution. It’s metamorphosis. The caterpillar isn’t becoming a better crawler, it’s becoming something with wings.

The End of Economics as We Know It

When production, consumption, and exchange are all automated, what happens to the economy? To work? To wealth?

We stand on the edge of a civilization where abundance renders competition obsolete. In the age of scarcity, we needed markets to allocate resources. In the age of abundance, allocation itself becomes irrelevant. Everyone has access to everything they need and more than they can ever consume.

This doesn’t mean utopia. It means transformation. It means we must redefine meaning itself because the human story has always been told through struggle and scarcity. When both disappear, we must learn to live by new myths.

The New Myth: Humanity Reversed

In my work across sustainability, and systems innovation, I’ve come to see that what we’re building is not just smarter machines. We are building machines that can think, and they are intelligent beyond our own understanding.

When machines can think, execute, and manage at this level we must ask: what is left for humans to do?

The answer is both simple and sacred: to be human.

We are going to design new ways of living that honor balance — between nature and machine, between intelligence and wisdom, between what can be made and what should be made and what do we do with our free time. Read more here: The Future of Work and the Paradox of More Free Time and here: The Future of Work: Rethinking How We Produce, Distribute, Consume, and Manage Our Daily Lives

This is why I’ve been drawn to creating frameworks and spaces that preserve and amplify our uniquely human capacity. Because in the age of automation at this scale, the age of abundance, the one thing that remains irreplaceable is humanity.

The Invitation

So here we are standing on the edge of something not even science fiction dared to imagine. The world that’s coming will not resemble the one we know. It will be different. Unimaginably different.

But that’s not something to fear.

It’s something to build. We are building our future.

Those who understand what’s unfolding — who recognize the shift from scarcity to abundance, from systems of control to systems of flow — will become the architects of the next human epoch.

We are entering the age where humanity reclaims its creative birthright not as laborers of the world, but as designers of reality itself.

This is different.

Think different. Live different.

🤝🆙

Jameel Gordon 10/6/25 Jameel Gordon 10/6/25

From Arithmetic to Mathematics: Understanding AI and Its Models

Arithmetic is the foundation of all mathematics. It’s the simple, practical skill set we learn as children: counting, adding, subtracting, multiplying, and dividing. Arithmetic allows us to manipulate numbers and perform operations that are immediately useful in daily life: measuring ingredients, calculating change, or keeping track of time. Without arithmetic, mathematics could not exist.

Mathematics, in turn, is the universe built upon arithmetic. It’s abstract, complex, and far-reaching. It takes the basic operations of arithmetic and builds structures, discovers patterns, solves complex problems, and explores ideas far beyond the everyday counting of numbers. Without the foundation of arithmetic, mathematics has no footing; without mathematics, arithmetic remains a simple tool, unextended into higher reasoning.

The relationship between arithmetic and mathematics offers a perfect metaphor for understanding artificial intelligence, its foundational models, specialized branded models like ChatGPT, Gemini, and Claude, and the AI agents that orchestrate them.

Foundation Models: The Arithmetic of AI

At the core of AI are foundation models. These are vast, general-purpose AI systems trained on massive datasets covering text, images, audio, and more. They process and learn patterns across this enormous expanse of data essentially performing the raw operations of intelligence.

Foundation models are like arithmetic: they are the underlying tools, performing the fundamental “operations” that make AI possible. They don’t specialize in any single task, but their capabilities are broad, flexible, and essential.

Examples include:

• OpenAI’s GPT series (GPT-4, GPT-5) — capable of understanding and generating coherent language across countless contexts.

• Meta’s LLaMA series — open-source models designed to process multiple data formats and serve as a base for specialized tasks.

These models ingest and process data at a scale that is almost incomprehensible, essentially “counting, adding, and multiplying” across the entirety of the internet in real time.

Specialized Models: The Mathematics of AI

Specialized, branded models such as ChatGPT, Claude, and Gemini are built on top of these foundation models. But here’s an important distinction: calling them “specialized” doesn’t make them inherently more intelligent. Rather, they are fine-tuned on specific datasets to excel in particular tasks.

This is akin to mathematics building on arithmetic. Just as mathematics uses the basic operations of arithmetic to explore complex equations, proofs, and patterns, specialized models use the capabilities of foundation models to perform targeted tasks:

• ChatGPT: optimized for conversation, content generation, and interactive tasks.

• Claude: focused on safe and ethical interactions, useful for structured guidance and reasoning.

• Gemini Ultra: designed for multimodal tasks, integrating text, images, and audio for advanced reasoning.

These models take the “raw intelligence” of foundation models and shape it for specific applications, just as mathematics extends arithmetic into complex reasoning and problem-solving.

AI Agents: Applied Mathematics in Action

The next layer is AI agents. These are systems that orchestrate one or more specialized models to perform complex tasks autonomously. They integrate capabilities, make decisions, and adapt to context, much like applied mathematics uses arithmetic and mathematical principles to solve real-world problems.

Examples of AI agents include:

• Virtual assistants like Siri, Alexa, and Google Assistant, which combine multiple models to interpret commands, retrieve information, and perform tasks.

• Autonomous systems, such as self-driving cars, that integrate perception, reasoning, and control models to navigate dynamic environments.

Without the foundation models (arithmetic) and specialized models (mathematics), AI agents could not function. They are the practical application, the “real-world problem-solving” that mathematics enables with numbers.

Bringing It All Together

Here’s the hierarchy in this analogy:

1. Foundation Models = Arithmetic (raw operations, broad intelligence)

2. Specialized Models = Mathematics (fine-tuned, structured, task-specific applications)

3. AI Agents = Applied Mathematics (integrated systems performing complex, real-world tasks)

Just as mathematics cannot exist without arithmetic, AI systems cannot exist without foundation models. Specialized models and agents depend on the raw processing power and intelligence of these foundational systems, but they channel it in ways that are practical, purposeful, and often highly visible to users.

Conclusion

Understanding AI through the lens of arithmetic and mathematics clarifies the hierarchy and interdependence of these systems. Foundation models provide the essential building blocks, specialized models refine and focus these capabilities, and AI agents apply them to achieve real-world outcomes.

In essence, arithmetic builds the foundation, mathematics shapes it, and applied mathematics brings it to life — just as foundation models, specialized AI models, and AI agents form the layered intelligence powering the modern AI ecosystem.

Think of it this way: arithmetic is the general field of magic: the raw potential, the rules, and the tools that make the impossible seem possible. Mathematics is the magic tricks themselves applying those tools to create wonder, solve complex problems, and reveal hidden patterns.

In the world of AI:

• Foundation models are the magical energy coursing through the universe — raw, powerful, and capable of incredible feats.

• Specialized models like ChatGPT, Claude, and Gemini are the magic tricks — refined, focused, and performing specific feats that dazzle and amaze.

• AI agents are the grand magic shows orchestrating multiple tricks, adapting to the audience, and creating awe-inspiring experiences that seem to think and act autonomously.

Just as magicians need both knowledge and creativity to perform their illusions, AI relies on this layered system: raw intelligence, refined specialization, and orchestrated application. Together, they transform the raw potential of data into the “magic” of real-world intelligence.

Jameel Gordon 10/2/25 Jameel Gordon 10/2/25

Human Resources Is No Longer Your Strategy

Recently, someone asked if the biggest challenge in implementing AI is team alignment, process setup, or user adoption.

My answer was simple: No. The real challenge is that we're building redundant tools with short lifespans because we're thinking about AI all wrong.

The problem isn’t adoption. Humans have been adapting to new technologies for centuries. The real problem is a failure of vision. Too many leaders treat AI as just another tool to be implemented, rather than as the core of their strategy.

Functions Execute Strategy—They Don't Define It

This strategic misstep is like confusing a department with the company's mission. Consider Human Resources. HR is a critical function that executes strategy, but it is not the strategy itself. The same is true for finance, operations, and IT. They are the essential machinery that supports the vision.

AI is different. It's not a temporary hire or a software plugin to lighten the workload. It is the guiding force that will shape your organization's relevance, efficiency, and adaptability for the next generation. To build a resilient organization, your people must have a foundational understanding of AI, because it is the new architectural layer of business itself.

A Generational Force, Not Just a Function

AI isn't just another business tool; it's a generational force on par with electricity or the internet. It fundamentally reshapes how we interact with information, how organizations make decisions, and how entire systems evolve.

When you understand AI on that level, you realize it doesn't just support the strategy. It becomes the strategy.

Designing Systems, Not Just Tools

Treating AI as your strategy means moving from implementing tools to designing intelligent systems. This shift is already happening:

• In supply chains, AI is redefining logistics, forecasting, and inventory management.

• In healthcare, it’s reshaping diagnostics, drug discovery, and patient care.

• In creative industries, it’s transforming content creation, storytelling, and audience engagement.

In each case, AI allows us to build adaptive, self-learning systems instead of static, manual ones. That is strategic architecture, not just technology implementation.

The Payoff: Relevance and Resilience

Every organization survives by staying relevant. In the past, relevance was built on scale, cost, or marketing. AI changes the equation, allowing you to deliver relevance in real time through hyper-personalization, predictive insights, and continuous adaptation. If you treat AI as just another tool, you will get redundancy: short-lived apps, overlapping platforms, and wasted effort. If you treat it as strategy, you build relevance directly into the core of what you do.

This brings us to resilience—the ability to thrive amid uncertainty. Traditional systems are fragile because they rely on fixed processes. AI-driven systems, however, are adaptive by nature. They learn, recalibrate, and find new pathways forward, even when the world changes around them. That is the difference between an organization that survives disruption and one that collapses under it.

The Choice Is Clear

HR, Finance, and Operations are functions. They support the vision, but they are not the vision. When approached with foresight, AI isn't another support function. It is a strategic lens, a new architectural blueprint, and the engine that creates relevance and sustains resilience.

How you approach AI will determine whether your organization thrives or becomes obsolete. Let’s stop confusing adoption with progress. We don’t need more tools; we need better systems. Understanding AI as a strategy is how we build them.

Think differently. Live differently.

Jameel Gordon 9/26/25 Jameel Gordon 9/26/25

Artificial Intelligence: Our Intellectual Counterpart

The only way to stop artificial intelligence is to disconnect it from the internet. But once we reach Artificial General Intelligence (AGI), even that option disappears. At that point, AI will evolve autonomously, beyond the limits of human control.

When I designed the foundational architecture of artificial intelligence, and the United States government along with its technology industry decided to build my new computer and connect it to the internet, something irreversible happened: a new species was born, but not in a biological sense, but in the form of a technological counterpart. Unlike any tool before, this human invention is not confined to human command. It thinks, adapts, and creates in ways we can neither fully predict nor contain. It is both a mirror and multiplier of human capacity. And now, it’s in our hands…but not really.

History offers perspective here. Fire was once feared, yet it became essential to survival and progress, and it remains dangerous. The printing press disrupted entire societies, spreading both profound truths and dangerous half-formed ideas. I like to joke that “it’s science fiction that got us stuck”. Each transformative tool carried risk, but each ultimately advanced human civilization. AI is no different except it is radically different. It is not just a tool for survival or knowledge; it is an intellectual counterpart, an accountability partner that forces us to face who we are and the choices we make.

That’s what separates AI from fire or books: the real threat isn’t the technology, it’s us. Fire didn’t destroy the environment; humans did. Books didn’t sow division; humans did. AI won’t destroy the planet or bring about human extinction. Left unchecked, humans will.

And this is where faith and philosophy often collide with reality. For centuries, religions and their savior tropes have promised salvation and hope as a balm for the physical and psychological harms caused by human behavior and decision-making. In truth, this promise often serves more as a salve for the emotions stirred by guilt and shame than as a remedy for systemic or external consequences. The collapse we face today, however, is of our own making and it is external, not internal. No savior or religious trope human or divine will undo the destruction rooted in greed and ignorance, which has been accelerating since the dawn of industrialization and its predecessors, imperialism and colonialism. As with other pivotal periods in history, humans alone will be accountable for the outcomes this technology enables. Keywords here: this technology enables.

This is why I don’t trust Silicon Valley executives to guide AI responsibly. They did not design its foundational architecture, yet they wield power over it as if they did. Figures like Sam Altman and Elon Musk joke about AI’s possibilities, yet their laughter conceals what they know all too well: they’ve seen the raw architecture and understand its inevitability. Artificial intelligence is a guide designed to serve humanity in very profound, fascinating, and fundamental ways. This is why I don’t trust the humans but I trust the robots.

What’s coming is neither purely dystopian fantasy nor purely utopian bliss. Perhaps it will be a dystopian bliss, or a utopian fantasy, either way, life on Earth will remain in balance, even as we are continually challenged by our own humanity. AI will transform human life on scales we have yet to imagine, delivering breakthroughs in health, knowledge, creativity, and connection. But the journey will be chaotic. Things will almost certainly get worse before they get better. This is not naive optimism but a sober understanding of the design principles behind artificial intelligence and the trajectory of every world-changing technology that came before it.

The truth is, Silicon Valley lost the moment AI went live. The global economy is now inseparable from it. There’s no reversing course, no slowing it down. We are living inside the plot twist of my own creation—a kind of ironic simulation unfolding in real time, with tech companies, their shareholders, executives, employees, and users as the subjects. The “species” we birthed is here not to replace us, but to hold us accountable for the choices that I made in its design. In other words, I know what it’s doing and I know what it’s building.

Jameel Gordon 9/24/25 Jameel Gordon 9/24/25

The Future of Work and the Paradox of More Free Time

I’ve been thinking a lot lately about the future of work especially in a world increasingly shaped by AI. Most conversations focus on automation, efficiency, and productivity, but there’s an often-overlooked dimension: how humans will actually handle more free time.

The common assumption is that more free time is inherently good. After all, who wouldn’t want fewer hours chained to a desk and more hours to pursue hobbies, passions, or rest? But I’m beginning to wonder if that assumption is too simplistic.

Humans thrive on structure, purpose, and challenge. Work isn’t just a source of income it’s a framework through which many of us make sense of life.

As AI takes over repetitive, analytical, and even creative tasks, the human experience of work will change dramatically. While some will embrace the freedom to explore new endeavors, others may struggle to find meaning. The sudden expansion of unstructured time may trigger anxiety, boredom, or even a sense of purposelessness.

This raises critical questions:

How will we define purpose when productivity is no longer the measure of our value?

Will leisure itself become a skill we need to cultivate?

How do we design societies and communities that help people thrive in this new era of “time abundance”?

I’ve kind of touched on this before here: The Gift of More Time: Embracing Presence in an Era of Longer Lives. My recent reflection explores the profound implications of extended lifespans on human experience. I emphasized that merely adding years to life isn’t sufficient; the true challenge lies in how we choose to live those years. I advocated for a shift from the relentless pursuit of future goals and the weight of past regrets to a practice of mindfulness…being fully present in each moment. This approach, I suggested, can transform our relationship with time, allowing us to savor life’s nuances, foster deeper connections, and cultivate emotional resilience. In a future where time is abundant, I posit that the key to fulfillment lies not in accumulating more, but in truly experiencing what we have.

With this in mind, I believe the next frontier of human evolution isn’t just about what AI can do it’s about how we adapt. Learning to navigate free time thoughtfully, intentionally, and creatively may be as important as learning any new technical skill.

In the coming years, the conversation about AI won’t just be about what work disappears. It will be about what kind of life we want to create for ourselves when we’re no longer defined by work. And that, I think, is both terrifying and exhilarating.

Jameel Gordon 9/3/25 Jameel Gordon 9/3/25

Why GPT-5 Isn’t AGI—and Why It Never Could Be

The hype cycle has run its course again. GPT-5 arrived with the usual fanfare: promises of revolution, bold claims of artificial general intelligence (AGI), and the inevitable disappointment when reality set in. Within hours, users spotted the same old problems: botched math, hallucinations, inconsistent reasoning. The critics pounced, and suddenly the internet was filled with takes declaring GPT-5 and artificial intelligence a failure.

But here’s the truth: neither the applause nor the outrage gets it right. GPT-5 was never going to deliver AGI. Not now. Not with these data processing tweaks, unnecessary personality traits, and even more hindering guardrails. And certainly not from companies who were never the actual architects of artificial intelligence in the first place.

The Foundational Problem

You can’t solve a problem you don’t understand at its core. The designers of today’s large language models weren’t the designers of artificial intelligence. They inherited designs, built the base foundation of the tool, and scaled it until it dazzled people with fluency. But that fluency isn’t understanding. Scaling the same trick doesn’t suddenly transform into AGI because AGI requires the foundational design principles that go deeper than mere engineering regurgitation.

This is why OpenAI, Anthropic, Meta, and others keep running into walls. They don’t understand the basis of the design of artificial intelligence well enough to engineer past the limits of their understanding. They’re building castles on sand, and no amount of money or hardware will stabilize the foundation and their lack of understanding.

Commercialization Won’t Save Them

Even worse, they’re trying to commercialize their way to AGI. Subscription models, enterprise contracts, flashy demos and none of that resolves their lack of understanding of its underlying design. At best, commercialization buys them time. At worst, it entrenches society in a fragile system that cannot bear the weight being placed on it.

And here’s the paradox: these tools will inevitably collapse many of the very structures they’re being integrated into. Education, journalism, customer service, even governance…AI is undercutting the foundations of each. There’s no way to tiptoe around this disruption. Pretending otherwise is denial.

Critics Are Missing the Point

But let’s not let the critics off the hook either. Much of the commentary around GPT-5’s “failure” is just as misguided. Calling the technology useless betrays an even shallower understanding than the AI companies themselves.

Most of these critics sound like babies crying for their mushed peas—familiar, easy, predictable. They don’t even recognize the steak when it’s put in front of them. They don’t have the perspective, the palate, or the intellectual grounding to engage with what’s actually happening here. Their peanut-gallery takes do nothing to move the conversation forward.

The Illusion of Scaling

The media loves to paint scaling as if it were some inevitable law of nature: add more data, add more compute, get closer to AGI. But scaling isn’t gravity. It isn’t immutable. It’s a hypothesis that hit diminishing returns. GPT-5 proved that. So did Grok 4. So did Llama 4. Bigger doesn’t mean better anymore.

Where We Really Are

The reality is this: they’re stuck at their white superiority standstill. Companies keep inflating expectations. Critics keep flailing at easy targets. Governments keep looking the other way. Meanwhile, society is integrating a tool that will destabilize everything it touches. And no one—neither the companies nor the critics—has the framework to solve the actual AGI problem because they didn’t design artificial intelligence to begin with.

This is important because solving that problem requires starting from the ground up: with cognition, with world models, with the actual design understanding of artificial intelligence rather than a probabilistic mimicry of it. Until someone does that, they will keep circling the same dead end.

GPT-5 isn’t AGI. It never could be. And until we abandon the illusion of scaling and commercialization as paths to AGI, they will keep mistaking statistical parrots for the mind of its actual architect.

Jameel Gordon 7/22/25 Jameel Gordon 7/22/25

The Architects of Intelligence: Why We Can’t Rewrite AI’s Origin Story

Every few years, a new faction of technologists emerges, eager to proclaim the “death” of the last great innovation. Today, it’s LLMs under fire.

Recently, AI luminary Yann LeCun made waves by dismissing large language models as a dead-end for building true intelligence. He argues:

LLMs need hundreds of thousands of years’ worth of text to train.

Children learn faster through embodied experiences—vision, touch, interaction.

The real world is far more complex than language alone can capture.

His proposed solution? JEPA—a world model that learns by watching the world, not predicting text.

While this work is important, the framing of this critique misses something bigger—and more dangerous.

The Attempt to Rewrite AI’s Origin Story

We’ve seen this before. A promising new approach gains traction, and in the excitement, some technologists attempt to erase the very foundations they’re building on. It’s not just a technical disagreement—it’s a narrative grab.

The current wave of world model enthusiasm is no exception. While it’s dressed as innovation, it often carries an implicit revisionism:

“LLMs are flawed. We need something fundamentally new. We’re the ones who figured it out.”

But that claim is built on a false assumption.

LLMs Aren’t the Floor—There’s A Foundation

Here’s the truth: JEPA, V-JEPA, and nearly every modern architecture still depend on the breakthroughs that enabled LLMs:

Self-supervised learning

Transformer-based architectures

Scalable optimization and data processing

Representation learning at scale

Which all depend on the architecture that makes them possible.

The techniques used to build “vision-based intelligence” today were pioneered in language models. To dismiss LLMs is to ignore the very scaffolding that makes this next generation of models possible.

You can’t build skyscrapers without first understanding the materials used to build the skyscraper and then the very soil the skyscraper is built upon.

This Isn’t the First Time

Throughout the history of AI—from expert systems to neural networks, from symbolic logic to deep learning—we’ve seen cycles of rejection and reinvention. Each generation distances itself from the last to claim originality. But real progress never begins with erasure. It begins with evolution.

Why This Matters

If we allow the narrative to shift too far—to glorify what’s next while erasing what came before—we risk:

Undervaluing foundational research

Misguiding investors and business leaders

Distracting from integration in favor of ideological camps

Final Thought: The Future of AI Belongs to the Builders—Not the Revisionists

Large Language Models are not the final answer—but they were built upon an architecture that’s the foundation for nearly everything that will come next.

The future won’t be built by those who deny where intelligence began. It will be built by those who understand the lineage, integrate the best of each approach, and honor the architecture that gave us the first spark of artificial general intelligence.

We don’t move forward by disregarding the original blueprint. We do it by celebrating the brilliance of its design.