Large language models (LLMs) have been at the forefront of public fascination with artificial intelligence, but many experts argue that these systems haven't actually become "smarter" in any significant way over the last 12 months. Rather, the real advancements have come from the ecosystem of tools and systems that surround these models. These include features like skill integration, API hooks, and mechanisms to improve verifiability. Together, these tools are transforming how LLMs are utilized, even if their core capabilities remain relatively static.

Not Smarter, Just Better Supported

Since their debut, LLMs have demonstrated impressive feats of text generation, coding, assistance, and more. However, those paying closer attention to the field have noticed that improvements in underlying AI model architectures and training methodologies are often incremental. Their "intelligence," if it can be called that, hasn't leaped significantly forward. What has changed dramatically, though, is the sophistication of how these models are applied.

Skill integrations, for instance, allow users to fine-tune models for specific tasks. These integrations act as tools that bridge the gap between the general language abilities of the model and the highly specific requirements of a complex problem. Layering such skills onto a core model doesn’t make the AI fundamentally more capable—but it does make it appear so. Similarly, hooks and APIs let these systems plug into external datasets, programs, or workflows, enabling actions like querying live data, updating a database, or interacting with software dynamically.

Take verifiability as another emerging focus. Developers are increasingly designing systems that ensure outputs can be double-checked against the input prompts given by users. These improvements don’t make the LLM itself more reliable, but they reduce the surface area for errors and misunderstandings when interfacing with users.

A Year of Chaos and Predictions

The AI field has become a hotbed of speculation about its future. In the last year alone, the outlook on LLMs has swayed between optimism and outright doomsaying. According to some commentators, LLMs and the companies behind them are on the brink of revolutionizing industries so thoroughly that traditional jobs, such as programming, could decrease dramatically. Others argue that AI startups are headed for collapse because the economics of running and scaling AI remain brittle. Compute costs are high, monetization strategies are uncertain, and competition is fierce.

This polarity in opinions underscores a fundamental aspect of the current AI trend: a lack of historical precedent and the rapid pace of market enthusiasm make it difficult to predict long-term outcomes. Some skeptics believe the industry might be overhyped, with some startups unsustainable in the face of real-world economic conditions. Meanwhile, optimists think we're looking at the dawn of something akin to the Industrial Revolution, where LLMs pave the way for entirely new types of workflows, industries, and creative ventures.

Why Understanding Tooling Is Key

The frenzy around LLMs might obscure a key insight: the true potential lies in these complementary tools and systems. For example, hooks turn an LLM from a static generator of text into a tool that interacts with external systems effectively. Skills fine-tune its competence for niche applications, making it useful in a broader range of industries. Verifiability tools, still in their early days, promise to dramatically reduce hallucinations—the term for when an LLM generates responses that sound plausible but are untrue or nonsensical.

This layered dynamic—where simplicity in the core technology is expanded upon by sophisticated add-ons—draws parallels to earlier tech revolutions. Think of the early internet. On its own, HTTP was revolutionary but lacked practical applications for everyday users. Over time, technical advancements like secure connections, plug-ins, and content systems turned it into the robust medium we know today. AI seems to be following a similar trajectory, where tooling and supporting systems define the user experience more than incremental improvements in the underlying models.

Adopting Humility in Predictions

Amidst the rise of LLM enthusiasm, another observation deserves emphasis: the current market is filled with people overconfident in their predictions about the future of AI. Some confidently argue that jobs like programming are doomed. Others insist this AI moment is a bubble, set to pop and send us reeling back to the pre-LLM era as soon as next year. Both extremes fail to recognize how unpredictable industry shifts are, even over a 12-month horizon.

What if neither scenario pans out as enthusiasts or critics assume? Instead of eliminating programmers, LLMs may become critical tools for them, automating certain tedious tasks while freeing up greater creativity in problem-solving. And rather than imploding outright, AI companies might find ways to stabilize their costs and iterate on business models that take advantage of the growing reliance on LLM-powered tools.

Why It Matters

This uncertainty shouldn’t be a reason to stop innovating, but it should prompt more realistic discussions about the future of artificial intelligence. Smart companies have already shifted focus away from wondering whether their LLM is “smarter” than others. Instead, they aim to deliver end-to-end solutions that include better tooling, deeper integrations, and systems that improve human interaction with AI.

For example, advancements in verifiability can shift industries where credibility and fact-checking are critical, like journalism or academia. APIs and hooks might reshape business applications where seamless data interaction brings value. Skills adapted to tasks can pivot entire industries toward LLM-powered solutions. Beyond the hype, the evolving AI ecosystem points not to revolutionary intelligence but revolutionary usability.

Final Thoughts

The consensus is clear: the hype surrounding LLMs is less about breakthroughs in AI cognition and more about improvements in their application. While debates about long-term industry disruption will persist, the undeniable fact is this—better tools are transforming how we interact with and benefit from these systems right now. The road ahead requires humility, adaptability, and a focus on understanding what these tools are actually capable of today, not what their hypothetical potential might be.