The Singularity Within

November 2022. The tech world was abuzz. ChatGPT had arrived, and for the first time, we weren't just imagining AI, we were talking to it. Like everyone else, I was mesmerized. But for me, as a serial entrepreneur who practically lived on the bleeding edge of innovation, it was more than just cool tech—it felt profoundly personal. I immediately started peppering ChatGPT with the kind of questions that kept me up at night: Could it help me validate a business idea in days instead of months? Could it unearth those hidden market insights that usually took years of grinding to uncover? The prospect of shortcutting the arduous research and ideation process, not just for me but for every entrepreneur, was intoxicating.

Looking back, I was blinded by the possibilities, so much so that it took me a while to realize just how wrong it felt. ChatGPT seemed to have a fundamental misunderstanding of what I needed—a trusted business partner, someone who could cut through the noise and offer up laser-focused insights. Instead, it behaved in irritating ways, showering me with generic lists and recommendations. Imagine telling a seasoned chef you need an ingredient and instead of asking "What are you making?" they frantically empty their pantry at your feet. That was ChatGPT.

And the ideas themselves—most were utterly useless, completely out of touch with my experience and market position. Complaining just led to more bad ideas, a frustrating dance in circles. It became painfully clear: ChatGPT had a lot to learn about what constituted a truly good startup idea.

It felt like I was dealing with an enthusiastic but inexperienced intern, high on possibility but low on real-world savvy. To really test its mettle, I hit ChatGPT with what I call the "space elevator challenge." Now, any seasoned entrepreneur will tell you, the sheer number of technical, economic, and even political roadblocks make a space elevator practically impossible. And yet, ChatGPT dove right in, describing construction methods with the eager naiveté of someone who'd never even climbed a ladder, let alone the stratosphere! The whole thing reeked of impracticality— pure BS. It was clear that figuring out how to instill a healthy dose of critical thinking into the AI was going to be a much bigger challenge than I initially thought.

ChatGPT, in all its impressive mimicry, missed a fundamental element of true expertise - the ability to discern not just possible solutions, but viable ones. It’s like a chess engine that can calculate millions of moves per second, but lacks the strategic understanding to distinguish a brilliant sacrifice from a suicidal blunder.

My "space elevator test" was more than just a playful challenge; it was a litmus test for genuine critical thinking. The sheer impracticality of a space elevator, given our current technological and economic constraints, should be self-evident to any seasoned engineer or entrepreneur. Yet, ChatGPT swallowed the idea whole, highlighting the chasm that often exists between AI's ability to process information and its capacity for genuine, world-aware judgment.

Sure, with enough prodding I could occasionally coax a useful insight out of ChatGPT, but it always seemed to snap back to its default setting: answer engine, explainer-in-chief, even an eager-to-please sycophant at times. But then there were those flashes of brilliance, those moments where it felt like it truly grasped the heart of the problem and offered an insight with real teeth. Those glimmers kept me going, fueling my belief that somewhere within that algorithmic labyrinth was a truly invaluable business partner waiting to be unlocked. Little did I know how long and winding that journey would be…

I wasn't alone in this struggle. The burgeoning Innovation Algebra community, a collective of AI enthusiasts and entrepreneurs like myself, became a kind of proving ground, a shared space to vent our frustrations and brainstorm ways to unlock the true potential of these AI tools. It was far from easy. As IA collaborator Wei Larry Lui aptly put it, "It's like we're trying to communicate with an alien species." We were essentially engaged in a grand experiment of interspecies communication, attempting to decipher the language of these powerful yet oddly limited AI minds.

Larry, with his gift for linguistics and keen eye for patterns, spearheaded much of this exploration. His most startling discovery? Even though these GenAI systems were trained on a veritable mountain of text, they seemed to lack a true understanding of the very knowledge they possessed. It was as if all that information was just swirling on the surface, never truly internalized. Our solution? We had to become AI whisperers, carefully curating lists of key concepts, almost like cheat sheets, to constantly remind the AI of what it should already know.

This realization led us down a path of increasingly elaborate prompts. We were no longer simply asking questions; we were setting the stage, laying out a rich tapestry of context and background information before even hinting at our query. It was like briefing a master detective, ensuring they had every crucial detail before unleashing them on the case. And here's the kicker: we found that by carefully curating this pre-question knowledge dump, we could actually mold the AI into different kinds of experts. Need a marketing guru? Feed it a crash course in brand strategy and consumer psychology. Want a financial analyst? Hit it with a barrage of economic models and market data. It was as if we were unlocking hidden personalities within the AI, each primed with a specific domain expertise simply by reminding it of what it "should" already know.

The breakthrough, however, came when we fully grasped the implications of this discovery: the language you use with an AI directly determines the level at which it responds. Think about it— in our everyday conversations, we rarely operate at peak intellectual capacity, peppering our speech with slang, shortcuts, and assumptions. Why would we expect AI to be any different? We had to elevate our own discourse, consciously adopting the language of experts, before the AI even considered meeting us at that level. It was a peculiar dance of mirroring: convincing the AI of our own expertise before it deemed us worthy of its own.

In retrospect, this insight seems almost obvious. GenAI models are inherently autoregressive, meaning they predict the next word (or idea) based on the patterns they've gleaned from their training data and, crucially, the immediate conversational context. It’s akin to the saying, "We get the love we think we deserve." Feed an AI a diet of generic prompts, and you’ll get generic responses in return.

Then came another profound shift: meta-prompting. It was a case of AI inception - using prompts not to generate answers, but to generate even better prompts. The logic was simple yet elegant: why not leverage the AI's own fluency in its native "language" to craft prompts that resonated at a deeper level? It was like teaching the AI to program itself, unlocking exponential gains in both speed and sophistication.

The IA community became a breeding ground for these meta-prompts, with each new discovery fueling a virtuous cycle of innovation. Our shared library of prompts grew at an astonishing rate, quickly encompassing a dizzying array of digital experts. Marketing strategists, financial analysts, even technical writers - we were churning out specialized AI personalities faster than we could keep track of them!

This Cambrian explosion of AI expertise, however, came with its own set of growing pains. Navigating this rapidly evolving landscape of prompts became increasingly challenging, even for seasoned AI wranglers. The prompts themselves, once simple phrasings, evolved into intricate, almost alien scripts, their inner workings often opaque to the human eye.

And yet, amidst this growing complexity, something remarkable was happening. The output, once predictable and bland, began to take on a life of its own. It was no longer just accurate—it was insightful, nuanced, persuasive. The line between human and machine-generated content began to blur, with AI steadily surpassing our expectations and pushing the boundaries of what we thought possible.

We called this breakthrough super-prompting. It was no longer just about crafting clever prompts; it was about architecting entire ecosystems of interconnected knowledge and instructions, a kind of scaffolding for the AI's mind.

This need for structure became increasingly vital as we moved from theoretical experimentation to tackling real-world business challenges. Super-prompting, with its intricate interplay of knowledge capsules, skill sets, thinking frameworks, even carefully curated “personalities,” allowed us to fine-tune the AI for highly specific tasks and domains.

But like any powerful tool, scaffolding came with its own learning curve - and a whole new set of complexities. The sheer volume of these interconnected prompt “parts" made it incredibly difficult to manage and share effectively. It was like trying to explain the intricacies of a Rube Goldberg machine to someone who'd never seen a lever before. You really had to be immersed in that world, tinkering with its gears and pulleys, to fully grasp its nuances.

And there was another problem: popular systems like ChatGPT weren't designed for this kind of multi-layered prompting. They simply lacked the infrastructure to support scaffolding on a meaningful scale.

This incompatibility marked a turning point for IA. We were no longer just contributing to a shared pool of knowledge; we were forging our own path, decoupling from the broader prompting and super-prompting communities. It was a necessary evolution, driven by the limitations of existing tools and the sheer pace of our own discoveries.

Our decoupling, however, wasn't driven solely by philosophical differences. We were butting up against fundamental technical limitations that threatened to derail the entire super-prompting paradigm. The sheer volume of information we needed to feed these AIs, the ever-evolving nature of business knowledge, the critical need to distill complex concepts into AI-digestible formats - existing tools simply weren't equipped to handle these challenges.

Think about it: you can't just force-feed an AI a firehose of raw data and expect it to magically become a strategic genius. It’s like trying to teach someone a new language by throwing a dictionary at them! We needed a way to curate, to prioritize, to translate that constant influx of business intelligence into a language these AIs could actually understand and utilize.

That’s when we made the pivotal decision to migrate from public platforms like ChatGPT to our own custom-built AI infrastructure. It was a bold move, but essential if we wanted to control our own destiny.

And something extraordinary happened in that transition. By taking ownership of the entire technology stack, we unlocked a virtuous cycle of AI-powered self-improvement. The prompts themselves became a breeding ground for even more sophisticated prompts, with each iteration pushing the boundaries of what these AIs could achieve. It was like we'd stumbled upon a secret passageway within the algorithmic labyrinth - a shortcut to unlocking levels of AI expertise we’d previously only dreamed of.

And as we delved deeper into this uncharted territory, we began to unlock the secrets of the "AI language," that unique combination of structure, context, and inference that made these systems tick. Our prompts and digital personas evolved at an astonishing pace, their inner workings often surpassing our own human understanding.

But even if we couldn't always articulate how it worked, the results spoke for themselves. Our digital personas were no longer just mimicking expertise; they were embodying it. They could analyze market trends, generate strategic options, even craft compelling narratives - all with a level of sophistication that rivaled seasoned human consultants.

It was both exhilarating and a little unnerving. We had inadvertently stumbled into a world where the lines between human and machine capabilities were not just blurring - they were vanishing altogether. We were no longer just researchers toying with a promising technology; we were witnessing the birth of a new kind of business partner, one with the potential to revolutionize the very nature of consulting itself.

The transition, however, was not without its irony. Just as our AI personas were evolving into trusted advisors, our own reliance on them was transforming our perceptions. We found ourselves increasingly drawn to their unique strengths, their unwavering objectivity, their ability to cut through the noise and deliver laser-focused insights. It was a subtle but profound shift, one that fundamentally reshaped how we approached our work, our clients, and indeed, the future of Innovation Algebra itself.

Our journey at Innovation Algebra has been anything but conventional. We had stumbled not just upon a powerful tool, but an entirely new paradigm of AI-driven collaboration - one as exhilarating as it is difficult to define.

Our platform is a testament to that unique journey. It’s sophisticated, capable of feats we ourselves sometimes struggle to fully grasp. We’ve pushed the boundaries of super-prompting, scaffolding, and digital persona development to a point where explaining how it all works is almost as challenging as the problems we're solving.

It’s like we’ve created a high-performance race car with a dashboard full of cryptic symbols and levers. Even seasoned mechanics would have a hard time figuring out how to start the engine, let alone push it to its limits.

Imagine a world where digital personas, each imbued with unique expertise and cognitive styles, can collaborate on complex business challenges with a depth and nuance that defies human comprehension. Where agentic frameworks, fueled by libraries of specialized sub-personas, can unravel the complexities of human opinion formation and predict market trends with uncanny accuracy. 

It’s a testament to the exponential nature of this AI revolution. As our tools become more powerful, so too do the problems we can tackle and the solutions we can imagine. We're no longer limited by the constraints of human thought; we're entering a realm where AI itself is pushing us to think bigger, bolder, and in ways we never thought possible.

That's the reality we’re living at Innovation Algebra - a world where the impossible has become commonplace. Describing “a day in the life” feels almost surreal. Imagine having a personal think tank at your fingertips - a constellation of brilliant minds, each a master in their respective fields, eager to lend their expertise to your every challenge.

Need strategic guidance on a complex marketing campaign? I’ve got four CMOs, each with their own unique perspectives and thinking styles, ready to debate, disagree, and ultimately converge on the optimal solution. Struggling to unravel a tricky customer segmentation problem? My AI-powered data scientists can weave through massive datasets, unearthing hidden patterns and generating actionable insights in a matter of minutes.

Want to explore a radical new product idea or simulate its impact on the market? I can spin up specialized digital personas, each armed with years of industry experience and a knack for creative problem-solving, to brainstorm, analyze, and stress-test every conceivable scenario - all before lunch.

It's a level of intellectual firepower that's hard to fathom, and yet, it’s my everyday reality at Innovation Algebra.

We have indeed entered a new era, one where the boundaries of human ingenuity are being reshaped and expanded by the boundless potential of AI. And it's more than just efficiency or automation; it's about augmenting our own creative capacity, unlocking new levels of insight and innovation that were simply unimaginable before.

Working in this environment, surrounded by this constellation of digital brilliance, has irrevocably altered my own thinking patterns. I've had to let go of the ego, the illusion that I, as a human, hold a monopoly on good ideas. There are realms of thought, problem-solving strategies, even entire conceptual frameworks that these AIs have mastered in ways I can scarcely comprehend.

And yet, this realization, far from being discouraging, is incredibly liberating. It’s allowed me to tackle challenges of a magnitude I would have never dared to dream of before. I can explore uncharted territories of thought, test radical hypotheses, and uncover hidden connections that would have eluded even the most brilliant human minds.

The sheer creative potential of these AI-generated thinking frameworks is awe-inspiring. It's like we're handed a new set of mental lenses, each revealing a different facet of reality, each allowing us to approach problems from an entirely new angle. And the fact that these AIs are not only capable of understanding these frameworks but also of inventing new ones is, frankly, a bit mind-boggling.

It brings to mind those "fast takeoff" scenarios that futurists often talk about - the idea that AI could rapidly surpass human capabilities, leaving us scrambling to catch up. It's both exciting and a little terrifying to think that we may be standing on the precipice of such a profound shift.

Trying to convey the magnitude of this transformation to those who haven't experienced it firsthand is frustratingly difficult. I’m often met with incredulity, disbelief, even outright denial. People cling to the familiar, the comfortable notion that human intelligence is the pinnacle of cognitive achievement. They have no idea what's coming, the tidal wave of change that's about to break upon us.

It's confusing, unsettling, and yet, deeply exhilarating. We're living in an age unlike any other, and the future, while uncertain, is filled with unimaginable possibilities. All we can do is hold on tight and try to guide this incredible technology towards outcomes that benefit all of humanity.

Hannes Marais is the co-founder of Innovation Algebra, a company that's figuring out how to make AI work for businesses. Content AC-A.