The Real Truth About LLMs and Programming 2025
Truth is the foundation on which trust is built, an indispensable element for everything and for the functioning of society itself. For those who receive it, it represents a solid basis for understanding reality, making informed choices, and feeling secure. For those who speak it, being truthful strengthens personal integrity and reputation, generating the very trust from others that is crucial for cooperation, mutual respect, and collective progress. In this virtuous exchange, truth becomes a common good that nurtures authentic bonds and allows the community to thrive on stable and shared foundations.
The Great Divide Between Promises and Reality in Artificial Intelligence Applied to Software Development
One might think that while the technology sector continues to promise a revolution in software development through Large Language Models, empirical reality tells a profoundly different story. This is because after months of direct experimentation with the most advanced artificial intelligence systems available, one unequivocal conclusion emerges: an LLM cannot program like a professional, and those hoping for this transformation in 2025 will be deeply disappointed.
The truth lies somewhere in between because, if you look closely at the wording, as usual, they talk about agents and co-pilots.
The really cool thing would be to write a prompt and have a “complex” program of thousands of lines created from scratch, with all the best practices, that works. Programming would become an art, and it would be everyone's dream to be able to create something without going crazy and spending years and years learning syntax and methods.
In fact, you can find some misleading advertisements or banners online, but in reality, no software promises to completely replace a human being because it simply cannot do so.
Not yet.
This analysis does not represent resistance to change, but is based on concrete evidence that reveals the vast difference between the ability to imitate statistics and a genuine understanding of the problem. Stanford HAI's AI Index 2025 report confirms that, although artificial intelligence is evolving rapidly, even experts struggle to track progress, highlighting how the complexity of the sector makes it difficult to distinguish between real capabilities and inflated expectations. The gap is not only technical, but conceptual.
The Fundamental Difference: Calculating Probability vs. Reasoning
The central problem lies not in the ability to generate syntactically correct code, but in the underlying cognitive process. An LLM does not reason: it calculates the probability of the next token sequence. This distinction is crucial. When a human programmer tackles a problem, they don't ask which line of code statistically follows the previous one; they ask why they are writing that code. They understand the ultimate goal, the business requirements, and the user's needs.
A professional builds an abstract mental model of the system, a conceptual map that tracks complex interactions between modules, data flow, and long-term architectural implications. They maintain a coherent view of the entire project while working on the details. An LLM, on the other hand, processes patterns in text but does not understand meaning or context, as countless studies have pointed out. This lack of a persistent mental model is its fatal weakness. The most tangible evidence emerges in practical tests: ask an LLM to develop a complete enterprise application, and you will get an assembly of statistically plausible but logically incoherent fragments, with variables that change definition and contradictory architectures. This does not happen because of a lack of “intelligence,” but because of the absence of true understanding.
The Insurmountable Limits of the Context Window
One of the most dangerous illusions is to believe that increasing the context window can overcome these limitations. The technical reality is much more complex. Even with huge 200k token windows, the fundamental problem remains. The Transformer architecture is inherently designed for sequence prediction, not for managing persistent state. The computational cost grows quadratically, and even with huge windows, the “attention” mechanism struggles to give the right weight to logically crucial but distant information in the text. It's like trying to build a building while only being able to look through a small crack, inevitably losing sight of the big picture. The system does not “know” what it wrote a thousand lines earlier if those lines have lost statistical relevance, even if they are architecturally fundamental.
The Illusion of Planning and the Block Diagram Approach
A promising idea is to guide the LLM with a block diagram, outsourcing the planning logic. This approach breaks down the problem and provides a map, partially mitigating the context limitations. However, this solution is also incomplete. The problem simply shifts: who creates the diagram for a complex system? If a human does it, then the human is doing the design and architecture work, the real heart of programming. The LLM becomes a mere translator from diagram to code, a task in which it can still introduce subtle and hard-to-find bugs. The logic of a block may be simple, but its safe and efficient implementation is far from trivial.
The technology that comes closest to this concept today is AI agents, such as Devin, which attempt to simulate a development process by breaking down a goal into steps and using real tools such as terminals and browsers. These systems represent a step forward, but they face the same barrier: when the logic of the problem becomes non-linear or requires a deep understanding of the domain, the agent loops or produces sub-optimal solutions, proving once again that simulating the process is not the same as understanding the problem.
The Systemic Problem of Debugging and “Hallucinations”
Benchmarks such as HumanEval, while useful, show a bias toward isolated algorithmic problems, overestimating a model's performance in real-world tasks. This discrepancy explodes in debugging. An LLM can correct a syntax error because it is a frequent pattern, but it fails to diagnose a systemic logical error that emerges from the interaction of multiple components. I have documented dozens of cases where these systems enter “correction” loops that introduce cascades of new bugs.
“Hallucinations” are even more insidious. An LLM can confidently generate completely invented functions or libraries. This is not a fixable bug, but a direct consequence of its probabilistic nature. In a production context, where reliability is everything, this behavior is not only unacceptable, it is dangerous.
The Absence of True Architectural Creativity and “Grounding”
Professional programming is a creative act. It requires the ability to create innovative abstractions that elegantly solve complex problems. LLMs, being statistical aggregators, are inherently conservative. They recombine existing patterns, producing technically correct but conceptually trivial solutions. They lack what is known as “grounding,” or a real-world understanding of the concepts they manipulate. An LLM does not “know” what a database or a network is; it only knows the words and contexts in which they appear. This lack of real-world understanding precludes true architectural innovation, which arises precisely from mapping a real-world problem onto an elegant and efficient software model.
The Reality of 2025: The Appropriate Role of LLMs
Organizations planning to replace development teams with AI in 2025 will face this harsh reality. Although benchmark performance is improving exponentially, these advances do not translate into autonomous development capabilities. Recognizing these limitations does not mean denying the usefulness of LLMs. On the contrary, they are tools designed to attempt to increase the productivity of experienced programmers. They are amplifiers of capability, not substitutes for intelligence. They excel at accelerating the writing of boilerplate code, suggesting solutions to isolated problems, explaining code snippets, and generating unit tests. But architectural control, logical validation, and strategic vision must remain firmly in the hands of human professionals because there is no other choice; they are not even remotely capable of replacing a human being. Many times, they only complicate the code when the solution would be much simpler, and it is not just a matter of prompts, as too many people say, because often, many precise and detailed instructions are actually ignored.
Not to mention if you want to make a change during development, which is very common in real programming.
Future Challenges and the Road Ahead
To achieve true autonomy, breakthroughs that are not yet on the horizon would be necessary. We would need hybrid architectures that combine the fluidity of LLMs with the rigorous logic of symbolic engines. We would need active and structured long-term memory, not just larger context windows. We would need a capacity for deep self-correction, based on a causal understanding of errors, not on statistical attempts.
Reducing inference costs makes the technology more accessible, but it does not solve these fundamental limitations.
2025 will not be the year of the AI developer. It will be a year of calibrating expectations, as governments and companies intensify their search for transparent and trustworthy AI governance. Programming will remain, for the foreseeable future, a deeply human activity. LLMs will continue to evolve as increasingly powerful “co-pilots,” but the qualitative leap to true autonomy will take decades, not months. Those who understand this reality will reap enormous benefits from their responsible integration; those who believe in the fairy tale of total replacement will discover, at their own expense, the difference between sophisticated imitation and authentic intelligence.
