The end of AI slop is coming

When I moved to Silicon Valley in 2023, everyone had the same question: "Why aren't you starting an AI content company?"

With Jasper and similar platforms raising massive rounds, it seemed like the obvious play for someone with my content marketing background.

I always said no for two reasons:

1. I didn't need to start another company (my investments were doing pretty well).

2. More importantly: AI simply couldn't produce genuinely good content

AI was missing two critical ingredients that make content worth reading. But now, for the first time, that's changing…

AI-generated content has been often dismissed as "slop." Tools like Jasper and Copy.ai promised to revolutionize content creation but largely fell short. The reason is simple: they lacked the ability to reason and experience—two crucial ingredients for producing truly engaging content.

Think about it: the most compelling content comes from two key elements—new information and a fresh perspective, both of which require reasoning and experience. AI has struggled with this because, until recently, it couldn't truly reason or draw on experiential data. It was good at remixing existing information but not at generating genuinely original insights.

What Makes an Insight Valuable?

Paul Graham has argued that the best insights are both general and surprising. A good idea should apply broadly, revealing something fundamental that others can recognize across different contexts, while also being unexpected enough to challenge conventional wisdom. Most AI-generated content has failed to meet this standard because it lacks the depth of reasoning necessary to produce truly insightful analysis.

Ben Thompson's Stratechery exemplifies this principle. His best essays don't just report on industry trends; they extract general principles from specific events—like the aggregation theory, which explains how digital platforms accumulate power in ways that traditional businesses never could. His insights are surprising because they often reframe the reader's understanding, yet general enough that they apply across industries.

Similarly, Marques Brownlee (MKBHD) does more than review gadgets—he derives principles about user experience, product design, and technology's impact on daily life. His videos don't just say this phone is good; they uncover broader patterns, like why hardware matters less than software ecosystems or how small design decisions shape user perception.

AI-generated content has typically failed to meet Graham's standard of providing general and surprising insights. It could summarize but not synthesize. It could provide surface-level observations but not challenge assumptions with truly original perspectives. That's because reasoning and experience are prerequisites for producing content that offers genuine value beyond what already exists.

The Reasoning Revolution

This is changing with the emergence of advanced reasoning models. Companies like Anthropic have introduced AI systems such as Claude 3.7 Sonnet, which features a hybrid reasoning approach that combines multiple methods to solve complex problems. This model's "extended thinking mode" allows the AI to self-reflect before answering, significantly enhancing its performance in tasks requiring deep logical thinking.

For example, when given a complex ethical dilemma about resource allocation, Claude 3.7 can now trace through multiple possible outcomes, weigh competing values, and articulate nuanced reasoning that considers both immediate consequences and long-term principles—a level of analysis that would have been impossible with earlier models.

Similarly, OpenAI's o3 model is designed to devote additional deliberation time to questions requiring step-by-step logical reasoning. Instead of rushing to an answer, these models can now plan ahead and reason through tasks methodically, performing intermediate steps much like a human expert would when solving complex problems.

This approach has yielded remarkable results in domains previously challenging for AI, such as:

• Mathematical reasoning: Working through multi-step proofs with formal logic

• Scientific analysis: Developing hypotheses based on experimental data

• Strategic thinking: Evaluating complex scenarios with competing variables

These models represent a significant shift from previous AI systems by incorporating self-assessment and internal deliberation, allowing AI to generate more nuanced and insightful content that's closer to human-quality thinking.

The experience revolution

In addition to improved reasoning models, AI agents and robotics are advancing rapidly. AI agents transform AI from a reactive tool to an autonomous actor capable of independently executing complex tasks. This evolution results in significant leaps in productivity and efficiency, enabling increased revenue, decreased expenses, and more innovation.

One of the most significant advancements in AI agents is OpenAI's Operator, a tool that goes beyond text generation to autonomously execute online tasks. Powered by the Computer-Using Agent (CUA) model, Operator can interact with web interfaces in a human-like manner—clicking buttons, filling out forms, and even navigating complex workflows. This ability allows AI to experience and act in digital environments, rather than merely processing static data. By integrating AI into practical applications like booking travel, managing online transactions, and assisting with research, Operator exemplifies how AI is shifting from passive computation to active participation in real-world tasks.

Robotics has also seen substantial progress, particularly in the field of humanoid automation. Companies like Figure AI are pioneering the development of humanoid robots such as Figure 02, which can perform tasks traditionally reserved for humans. Standing at 5 feet 6 inches, Figure 02 is designed to navigate human-centric environments, performing household chores, warehouse operations, and even assisting in healthcare settings. Equipped with the Helix visual-language-action (VLA) model, it can interpret commands, assess its surroundings, and execute complex sequences of actions autonomously. This represents a leap toward AI-driven physical interactions that mirror human experiences.

These advancements suggest that AI is moving beyond mere data processing to engaging in tasks that require a form of experience, thereby enhancing its ability to produce content grounded in practical application. The ability to interact with digital and physical environments not only increases AI’s utility but also allows it to generate richer, more informed insights—leading to the next wave of AI-generated content that is more thoughtful, original, and deeply integrated into human workflows.

The Future of AI-Generated Content

The ingredients for great content are finally in place. AI isn't fully there yet, but for the first time, the trajectory is clear. As models continue to evolve, AI-generated content will shift from remixing old ideas to discovering new ones. We're witnessing the transition from AI as a sophisticated parrot to AI as a genuine thought partner.

In the coming years, we can expect to see:

1. AI systems that generate truly novel hypotheses in scientific research

Advanced reasoning models will transform scientific discovery by identifying non-obvious connections across disparate domains that human researchers might overlook. Rather than simply processing existing data faster, these systems will propose entirely new research directions by recognizing patterns that span multiple disciplines. We're already seeing early examples in drug discovery, where reasoning-enhanced AI has identified novel molecular configurations based on theoretical principles rather than just recombining known structures. This represents a fundamental shift from AI as a research tool to AI as a genuine scientific collaborator capable of accelerating breakthroughs in medicine, climate science, and materials engineering.

2. Content platforms that produce original analyses of market trends or political developments

Business and political analysis will move beyond data aggregation to genuine strategic intelligence as AI develops the contextual understanding needed to identify underlying patterns beneath surface-level events. Next-generation analytical platforms will connect micro-trends across industries to predict macro-shifts before they become obvious, providing decision-makers with truly actionable insights rather than just information. These systems will excel at detecting subtle signals that telegraph major directional changes, uncovering the strategic logic behind market movements or political decisions that might otherwise remain obscured. The result will be content that provides genuine competitive advantage by revealing connections and implications that even expert analysts might miss.

3. Creative tools that don't just mimic existing styles but pioneer new aesthetic approaches

Perhaps most profoundly, reasoning-capable AI will break free from simple mimicry to develop its own aesthetic principles and creative approaches. Rather than just training on existing human works, these systems will identify underlying patterns and extend them in directions humans haven't explored, becoming genuine creative partners rather than sophisticated copying machines. We're seeing early indications in experimental systems that don't just imitate established styles but develop novel structures based on conceptual frameworks rather than mere references. In writing, music, visual arts, and design, this will manifest as AI that doesn't just sound or look like familiar creative works but represents genuinely new additions to our creative landscape—expanding human creativity rather than simply reproducing it.

This isn't just an upgrade—it's the beginning of a fundamental transformation in how we think about the relationship between human and machine intelligence. The era of AI slop is ending; the age of AI insight is just beginning.