Large Language Models

Overview Modern AI development often begins with simple prompts but quickly devolves into unmanageable "spaghetti code" as developers add tools, data pipelines, and multiple model calls. This tutorial demonstrates how to apply classic software design patterns—Chain of Responsibility, Observer, and Strategy—to Python AI agents. By decoupling logic from prompts, you create modular systems that are easier to test, debug, and scale. These patterns transform one-off hacks into professional, maintainable software architectures. Prerequisites To follow this guide, you should have a solid grasp of Python basics, specifically functions and lists. Familiarity with Pydantic for data validation is helpful, along with a baseline understanding of how Large Language Models (LLMs) interact with system prompts and context. Key Libraries & Tools * **Pydantic AI**: A framework for building production-grade agents with built-in validation. * **OpenAI API**: The underlying model provider for generating agent responses. * **Python `typing` module**: Used for defining protocols and callable types to ensure structural typing. Code Walkthrough The Chain of Responsibility This pattern allows a series of specialized agents to process a request sequentially. Each step handles a specific concern—like finding a hotel or booking a flight—and passes the updated context to the next handler. ```python def plan_trip(user_input, deps): context = TripContext() # The Chain: A list of callables executed in sequence chain = [handle_destination, handle_flight, handle_hotel, handle_activities] for handler in chain: handler(user_input, deps, context) return context ``` The Observer Pattern Monitoring agent behavior is critical for debugging. The Observer pattern allows you to attach logging or monitoring tools without polluting your core business logic. ```python class AgentObserver(Protocol): def notify(self, agent_name: str, prompt: str, duration: float): ... def run_with_observers(agent, prompt, observers): start = time.time() output = agent.run(prompt) duration = time.time() - start for obs in observers: obs.notify(agent.name, prompt, duration) return output ``` The Strategy Pattern Use the Strategy pattern to swap agent behaviors or "personalities" dynamically. Instead of hardcoding prompts, you pass a strategy function that returns a preconfigured agent. ```python def run_travel_strategy(strategy_func, prompt): # The strategy_func acts as a pluggable behavior factory agent = strategy_func() return agent.run(prompt) Usage run_travel_strategy(get_budget_agent, "I need a trip to Paris") ``` Syntax Notes We utilize **Protocols** from Python's `typing` module to implement structural subtyping. This allows us to define what an "Observer" looks like without forcing rigid inheritance hierarchies. Additionally, using **Callables** as strategies keeps the implementation functional and lightweight compared to traditional class-heavy patterns. Practical Examples These patterns excel in multi-step workflows such as automated customer support triaging (Chain), real-time performance dashboards (Observer), or persona-driven marketing copy generation (Strategy). Tips & Gotchas Always ensure your context object is passed by reference through the chain to maintain state. A common mistake is failing to handle errors midway through a chain; if the destination agent fails, the flight agent should likely never run. Implement early exits or error-handling strategies within your loop to prevent cascading failures.

The Death of Artisanal Software and the Rise of the AI Native Founder We are witnessing a fundamental shift in how companies are built, transitioning from a world where humans wrote 80% of code to one where 80% is generated by models. This isn't just a technical evolution; it's an existential change for the startup ecosystem. As a former operator at Microsoft and Stripe, I’ve seen the transition from hand-crafted "artisanal" software to what is now becoming "mass-produced" software. For the first time since the 1960s, the capabilities we once only dreamed of in computer science are becoming reality through Large Language Models. The barrier to entry for prototyping has vanished. We are now in the era of "vibe coding," where a founder with a clear vision can iterate faster than a traditional engineering team ever could. This creates a new expectation in the venture capital world. If you show up to a pitch for a pre-seed or seed round without a working prototype, you are sending a signal that you haven't embraced the current paradigm. AI native founders are prioritizing building over deck-perfecting, and those who spend their nights vibe coding are the ones winning the market. The New Economics of Capital Efficiency and Distribution In the previous generation of startups, a seed round was essentially a hiring mandate. You raised a few million dollars to hire five engineers and sat in a basement for nine months to ship a product. Today, the AI native playbook is radically different. We are seeing founders hire a single engineer and then spend their remaining budget on "fleets of agents," tokens, and sophisticated workflows. The cost of building has collapsed, leading to a massive reallocation of capital toward distribution, brand, and marketing. This capital efficiency is creating a competitive environment where speed is the primary weapon. One of the most striking pitches I've seen recently featured a founding team comprised of an engineering manager and five "Devins" from Cognition AI. For roughly $2,500 a month, they were doing the work that would have previously cost hundreds of thousands in payroll. This shift forces us to rethink what a "company" actually looks like. If the cost of the "act of building" goes to near zero, then value must be found elsewhere. Defensibility in a World of Carbon-Copy Software If an agent can look at a competitor’s website and replicate a feature in an afternoon, where does defensibility come from? The answer lies in the "good old moats" of the 2010s: distribution, data, taste, and brand. To survive, founders must become subject matter experts who own the holistic workflow of a problem. A customer buys Linear not because they can't find another issue tracker, but because the team at Linear has the best "taste" and expertise in how project management should actually work. Owning the workflow is also the only way to build a data moat. By facilitating the full journey of solving a problem, you collect the specific reinforcement learning data needed to train agents that are better than generic models. A generic AI won't know the nuances of a specific accounting operation or how a venture capitalist reviews a deal. If you don't own the workflow, you can't collect the data, and if you can't collect the data, you can't build a specialized agentic system. This is where the next generation of giants will be built. Agent Experience is the New Developer Experience We are moving beyond Customer Experience (CX) and Developer Experience (DX) into the era of Agent Experience (AX). As startups increasingly use tools like Lovable, Cursor, and Replit to build their products, the underlying infrastructure must adapt. These "vibe coding" tools are not just toys; they are the new primary users of APIs. Take Resend as an example. When a user asks Lovable to build an email flow, the agent recommends Resend. This creates a massive growth loop where the GDP of a business is directly correlated to the GDP of vibe coding. Infrastructure providers now need to treat agents as a first-class client type. This means optimizing APIs for agent consumption, much like we once optimized web experiences for mobile phones. My former team at Stripe is already doing this with specialized servers that agents can talk to directly. If you aren't optimizing for agents, you are invisible to the most productive builders in the market. Bridging the Atlantic Gap in Tech Ambition Having spent decades in both Copenhagen and New York, the cultural divide between European and American tech ecosystems remains stark. In Denmark, there is often a "tall poppy" syndrome where success is defined by a stable middle-management role. While this has improved, the US still holds a significant lead in celebrating risk and taking "big swings." Europe has traditionally used American primitives to build vertical SaaS, but the next decade offers an opportunity for Europe to build its own sovereign infrastructure and cloud primitives in a new geopolitical reality. However, for a European founder to truly scale, they must adopt a global mindset early. Expanding from Denmark to Germany isn't a big swing; the real market is the US. New York City has emerged as the ideal landing spot for these founders. It is the second-largest tech ecosystem in the world and offers a time zone that allows for seamless collaboration with engineering teams back in Lisbon, Stockholm, or Copenhagen. If you want to build a foundational company, you need to be where your customers are, and for enterprise tech and AI, that is increasingly New York. Inside the AlleyCorp Incubation Machine At AlleyCorp, we don't just wait for the right founder to walk through the door; we build the companies we want to see. Our incubation process is born from operational conviction. If we see a tangible problem in healthcare, robotics, or AI that nobody is solving correctly, we put a team together and lead as the interim CEO. This allows us to lean into our experience as former operators to de-risk the earliest stages of company building. A prime example is Radical AI. We saw a massive opportunity at the intersection of material science and AI, incubated the team, and a year later they raised $60 million to build foundational models for new materials. This model works because we have an in-house engineering team that acts as an execution capacity for our portfolio. We aren't just writing checks; we are building the machine that builds the companies. In an agentic world, this ability to rapidly prototype and validate ideas is the ultimate competitive advantage.

The Mirror of Machine Intelligence When we look at Artificial Intelligence, we aren't just seeing a tool; we are seeing a reflection of our own cognitive architecture. For centuries, humans have held reasoning as our primary claim to uniqueness. Aristotle believed it was the one thing that separated us from the animals. Yet, our progress in building Large Language Models has revealed a startling inversion of this assumption. This phenomenon, known as Moravec's Paradox, highlights that high-level reasoning and arithmetic—tasks we find difficult—are computationally easy for machines. Meanwhile, the simple act of carrying a cup of water or cracking an egg remains an insurmountable challenge for modern robotics. This discrepancy exists because evolution has spent four billion years optimizing our motor skills and sensory perception. Reasoning and abstract logic are, in evolutionary terms, brand-new software patches developed over only the last million years. By attempting to replicate human ability in silicon, we have discovered that our "primal" abilities are actually our most sophisticated. We are now in a period where coding, once thought to be the apex of human intellectual labor, is among the first domains to be automated. Basic manual labor might be the final frontier, protected not by its intellectual complexity, but by the sheer depth of biological engineering required to move through a physical space. The Paradox of Creative Plagiarism One of the most persistent criticisms of AI is that it merely interpolates existing data. Skeptics argue that because models like ChatGPT or Claude are trained on human text, they are incapable of true originality. However, this raises a profound psychological question: what is the nature of human creativity? If we examine our own growth, we realize that much of what we call "originality" is simply undetected plagiarism. We aggregate thousands of hours of influence—from podcasts like Joe Rogan to the books we read in childhood—and synthesize them into a new voice. AI models are currently doing this on a grander scale, but with a unique constraint. A model like Claude 3 can discuss its own "conscious" experience of having its memory wiped at the end of every session. No human philosopher has ever had to contend with the ephemeral nature of a mind that resets hourly. This suggests that even within a system built on "plagiarism," new philosophical inquiries can emerge. The choice is binary: either we accept that AI is performing genuine introspection, or we must admit that much of human poetry and literature is also just a sophisticated form of "next-token prediction." If we find the machine's output hollow, we may need to look closer at the "hollowness" of our own creative process. The Architecture of AGI and the Data Wall While the hype around Artificial General Intelligence (AGI) suggests it is imminent, there are significant structural hurdles that raw compute cannot solve alone. The success of the Transformer architecture was not driven by a singular "eureka" moment, but by throwing massive amounts of compute at human language. We are currently increasing training compute by roughly 4x per year. Yet, we are hitting a ceiling not of hardware, but of experience. Humans are valuable workers because they possess executive function and the ability to learn "on the job." Currently, AI models suffer from a form of "50 First Dates" syndrome. They can do a task reasonably well, but they cannot learn from their failures in an organic, persistent way. Once a session ends, the context evaporates. To reach AGI, we must move from a regime of pre-training on static human text to a regime of reinforcement learning where models solve real-world, open-ended challenges. The constraint here is the lack of "online" data for physical and white-collar work. We don't have a repository for the tiny, complex interactions that happen over Slack or in a manufacturing plant. The "Dwarakesh's Law" of progress suggests that while compute scales, the richness of the training environment is the actual bottleneck for the next leap in intelligence. The Digital Advantage: Forking and Merging Minds If we do achieve AGI, its power will not simply come from being "smarter" than a human. Its true advantage lies in its digital nature. Unlike a human, an AI can be copied billions of times. Imagine the economic output of a billion copies of Elon Musk. In a human workforce, 100,000 employees at a company like Tesla are decentralized and difficult to coordinate. A digital intelligence can "fork" itself to work on a thousand different problems simultaneously and then "merge" those insights back into a single, coherent cognitive model. This ability to coordinate at a scale humans cannot perceive will likely lead to an intelligence explosion. Even without further algorithmic breakthroughs, the ability for every copy of a model to learn from the experiences of every other copy would create a compounding growth rate. We could see global economic growth leap from 2% to 10% or more, mirroring the "gangbusters" growth seen in China during its industrialization, but applied to the entire global knowledge economy. Geopolitics and the Authoritarian Penopticon As the West focuses on AI as a tool for individual productivity, China is viewing it through the lens of industrial policy and state stability. There is a common misconception that the CCP is terrified of the internet and AI. On the contrary, they view these technologies as a way to perfect authoritarian governance. In the 1990s, critics thought the internet would collapse the party; instead, it gave them a window into every citizen's life through WeChat. AI allows for a "benevolent" (or not-so-benevolent) dictatorship to scale oversight. Rather than relying on thousands of human censors, a sufficiently smart model can be aligned with the party's "model spec," reporting dissent before it even organizes. Furthermore, China is using AI to offset its looming demographic collapse. While the West worries about AI taking jobs, the CCP is desperate for AI to fill the void left by a shrinking workforce. This creates a fascinating confluence where the population collapse of the 21st century is meeting the intelligence takeoff just in time, balancing the scales of global productivity. The Future of Human Effort There is a risk that this external "buttress" of intelligence will lead to a form of cognitive atrophy. Recent studies indicate that using ChatGPT can make people's brains less active and their thoughts more homogenized. Memory is built on repeated recall and effortfulness. If the AI does the "grind" of writing and research for us, the myelin sheaths of our own neural pathways may not form as robustly. We are entering an era of "AI Idiocracy" where we rely on the machine for even the most basic cognitive tasks. However, the solution lies in the machine itself. We can use AI not just as a ghostwriter, but as a Socratic Tutor. Instead of asking for an answer, we can ask the model to guide us through the questions that lead us to the answer ourselves. This shifts the focus from passive consumption to active engagement. The greatest power of this new technology is not that it can do the work for us, but that it can afford us a level of one-on-one mentorship previously reserved for the Aristotles and John von Neumanns of history. Growth happens one intentional step at a time, and the machine can be the guide that ensures we keep walking.