Understanding the Jagged Frontier of AI

I spend a lot of time with technical founders building AI companies. Many assume that if we just make models bigger and feed them more data, we'll eventually reach true intelligence. I see a different reality. The fundamental limits of transformer architecture run deeper than most founders realize. Transformer models face three architectural barriers that no amount of scale can solve: 1️⃣ The Edge Case Wall An example in autonomous vehicles: Every time you think you've handled all scenarios, reality throws a new one: a child chasing a ball, construction patterns you've never seen, extreme weather conditions. The architecture itself can't generalize to truly novel situations, no matter how much data you feed it. 2️⃣ The Pattern Matching Trap Our portfolio companies building enterprise AI tools hit this constantly. Current models can mimic patterns brilliantly but struggle to reason about new scenarios. It's like having a highly skilled copywriter who can't generate original insights. The limitation isn't in the training—it's baked into how transformers work. 3️⃣ The Semantic Gap LMs process text without truly understanding meaning. We see this clearly in technical domains like software development. Models can generate syntactically perfect code but often miss fundamental logic because they don't grasp what the code actually does. This creates a massive opportunity for technical founders willing to rethink AI architecture from first principles. Some promising directions I'm tracking: → World models that understand causality and physical interaction → Architectures designed for reasoning during inference rather than training → Systems that combine multiple specialized models rather than one large generalist Founders: While others chase marginal improvements through scale, focus on solving the fundamental problems to build the next $100B+ business (and I'll be your first check ;))

This is the signal cutting through the noise. "Everything they do is through mimicry, rather than abstracted cognition." AI Pioneer Gary Marcus didn’t just make an observation, he articulated the fundamental flaw of today’s AI paradigm. In doing so, he echoed the work of AGI Pioneer Peter Voss, who has been building real cognitive architectures for over two decades, architectures designed not to mimic intelligence, but to generate it from first principles. LLMs don’t build world models. They don’t learn like humans. They cannot adapt autonomously to unfamiliar environments or shift their behavior in response to changing goals. What they do at scale is refined statistical mimicry producing syntax without semantics, fluency without true understanding. And no matter how massive these models become, their core limitations remain the same: hallucinations persist, brittleness is ever-present, and rigidity prevents them from adapting in real time. More data doesn’t solve this. More compute doesn’t either. The real failure isn’t technical, it’s architectural, conceptual. The industry has conflated linguistic fluency with intelligence and, in doing so, has misdirected trillions of dollars toward systems that can impress in benchmarks but falter in reality. These models may ace the bar exam, but they crash the moment they’re asked to operate without a script in a novel, real-world context. As Peter Voss says "The true test of AGI isn’t passing a benchmark, it is when a college level AGI system can walk into an unfamiliar desk job, in a completely new environment, and learn, adapt, and perform like a human, without being explicitly retrained or hand-held." That kind of real-time generalization and autonomy is something LLMs are structurally incapable of achieving. But it is exactly what Aigo.ai team led by Peter Voss has been building toward, through architectures grounded in cognition, not just correlation. What this means is clear and urgent: we are sitting on a multi-trillion-dollar misallocation of capital and talent. We’ve built dazzling systems that generate fluent text at scale, yet collapse in production environments that demand reasoning, learning, and adaptability. The real opportunity is the one hiding in plain sight, it is not in scaling next-token prediction. It’s in building cognitive engines that can reason, learn continuously, and evolve over time like us. That's not a parameter tweak or a model refresh. It’s a paradigm shift. Peter Voss and team Aigo.ai have spent more than twenty years developing the blueprint for this next wave. That wave is no longer theoretical, it’s arriving now, at the inflection point we’ve all been waiting for, consumes a million times less resources with none of the limitations of LLMs while offering a direct path to AGI. If you believe the future of AI lies in cognition, not imitation, let's talk. Thanks again for sharing your insight Gary Marcus.

Most AI systems today rely on a single cognitive mechanism: procedural memory. That’s the kind of memory involved in learning repeatable patterns — how to ride a bike, follow a recipe, or autocomplete a sentence. It’s also the dominant architecture behind LLMs: self-attention over statistical embeddings. That explains a lot about LLM strengths as well as their failures. LLMs do well in what psychologist Robin Hogarth called “kind” environments — stable, predictable domains where the same actions reliably lead to the same outcomes. But they tend to fail in “wicked” environments—settings where the rules shift, feedback is delayed, and the right answer depends on context that isn’t explicitly stated. In those environments, procedural strategies break down. Humans rely on other mechanisms instead: semantic memory for organizing abstract knowledge, associative learning for recognizing useful patterns, episodic memory for recalling prior experiences. LLMs don’t have those. So they: ➡️ miss abstract relationships between ideas ➡️ fail to generalize across context ➡️ lose track of evolving goals ➡️ don’t build up any durable sense of what works and what doesn’t This isn’t a matter of more data or better training. It’s an architectural limitation. At Aampe, we’ve had to grapple with these gaps directly — because customer engagement is a wicked learning environment. That pushed us to move beyond purely procedural systems and build machinery that can form and adapt conceptual associations over time. Working on these problems has made me uneasy about how singular LLM cognition really is. If one mechanism were enough, evolution wouldn't have given us several.

𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘀𝗲𝗱𝘂𝗰𝘁𝗶𝘃𝗲. The promise of a hands-off SOC, where AI effortlessly sniffs out threats, is intoxicating. But relying solely on AI for detection and response? That's less "cybersecurity utopia" and more "digital disaster waiting to happen." Here's the harsh truth: AI's brilliance is tethered to its training data. Novel attacks often slip through its grasp. It's like expecting a history textbook to predict tomorrow's news. And let's not forget the deluge of false positives – AI mistaking perfectly normal activity for a full-blown breach. Beyond just identifying anomalies, true security understanding often requires context and nuance that AI struggles to grasp. Developers deploying a new application or pushing a large code update might trigger unusual network patterns and resource consumption. AI can flag these anomalies, but it lacks the insight into planned development cycles and authorized system changes to determine their legitimacy. Over-reliance on its alerts without human validation can lead to wasted resources chasing shadows or, worse, ignoring subtle indicators that a human analyst would recognize as malicious. And then there's the human element: intuition. AI can process data, but it can't think like an adversary. That's where human threat hunters come in, spotting the subtle anomalies that AI, in its data-driven arrogance, overlooks. So, while AI offers undeniable benefits, it's not a substitute for human expertise. It's a tool, a powerful one, but still just a tool. And tools, as we all know, can malfunction, misinterpret, and occasionally decide to stage a digital rebellion. #AISecurity #CyberSecurity

Does it actually matter which AI model marketers choose? Or are we overthinking this? The "jagged frontier" of LLM capabilities shifts constantly. Each model excels in different areas, often unpredictably. Some nail creative tasks but stumble on simple math. Others excel at research but produce mediocre copy. My personal stack has evolved through trial and error: 1️⃣ Claude for writing and data analysis 2️⃣ ChatGPT for deep research and image generation 3️⃣ Gemini for tasks requiring large content windows (analyzing hundreds of customer conversations) 4️⃣ Perplexity for searches of all kinds, including shopping 5️⃣ Fast tools like Bolt for product prototypes and Lovable for microsites PATTERNS AMONG EFFECTIVE MARKETERS As I am talking with marketing leaders, I've noticed two distinct approaches among marketing teams leveraging AI successfully: THE MULTI-MODEL APPROACH Some teams (like myself) aren't limiting themselves to one LLM. They're selecting the right model for each specific task, using ChatGPT's research capabilities for one project, Perplexity for another, then bringing everything together with workflow tools like Copy.ai or Zapier. THE STANDARDIZATION APPROACH Other teams (often guided by IT departments focused on security and compliance) standardize on a specific LLM like Gemini or CoPilot. This creates a shared learning environment where teams develop deeper expertise within a unified system. Both approaches are working. The multi-model teams gain flexibility and optimal performance for specific tasks. The standardized teams benefit from consistency, shared learning, and simplified workflows. The real question isn't which model you use, it's developing the judgment to know when to trust AI outputs and when to apply human oversight. In other words, I'd say understanding the "jagged frontier" of AI capabilities matters more than which specific models you adopt. What's your experience? Does the freedom to choose multiple LLMs create an advantage, or is adopting AI itself the key step forward, with the complexity of different models just creating noise? #ArtificialIntelligence #MarketingTech #AIStrategy