{"id":35934,"date":"2026-06-03T17:34:25","date_gmt":"2026-06-03T15:34:25","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=35934"},"modified":"2026-06-03T17:34:27","modified_gmt":"2026-06-03T15:34:27","slug":"is-the-human-the-bug-or-the-feature-of-ai-development","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/is-the-human-the-bug-or-the-feature-of-ai-development\/","title":{"rendered":"Is the human the bug or the feature of AI development?"},"content":{"rendered":"\n

Spoiler: Business is gently escorting us out of the door of AI processes. But maybe it’s not the end of our job\u2014rather, the beginning of something much more interesting.<\/em><\/p>\n\n\n\n

Do you remember when, to approve any change, you had to wait for the manager’s signature on a mimeographed document?<\/p>\n\n\n\n

\n

We need someone responsible.<\/em><\/p>\n<\/blockquote>\n\n\n\n

This was the phrase echoing in everyone’s mind, the invocation of someone to take responsibility for what was happening. Even today, Italy relies on this endless chain of approvals, which often freezes processes for hours, days, or even years.<\/p>\n\n\n\n

We are no longer surprised if a job promised in a year is eventually delayed by six months: it is normal.<\/p>\n\n\n\n

In modern AI-governed processes, we tend to imagine a similar scenario: every image produced by an LLM, every text, every comment should be supervised by a human. They sold us this approach with a reassuring formula: \u201chuman in the loop\u201d<\/em>.<\/p>\n\n\n\n

It was a fascinating idea that put humans at the center of the process. I believed in it too, hoping that human contribution could have a real weight in the value chain. However, what we see as value, the industry sees only as a cost. The era when humans were considered a fundamental resource within the workflow is setting at terrifying speed.<\/p>\n\n\n\n

For years, we were told that human-in-the-loop was our life insurance policy:<\/p>\n\n\n\n

\n

Don’t worry, machines are imperfect and human input will always be needed.<\/em><\/p>\n<\/blockquote>\n\n\n\n

But this narrative clashed with operational reality. Driven by an insatiable hunger for scalability and latencies that must drop below the millisecond threshold, business is gently escorting us to the exit of the operational cycle.<\/p>\n\n\n\n

Welcome to the era where the operator is no longer in<\/em> the loop, but at most on<\/em> the loop (human-on-the-loop<\/strong>), if not completely out<\/em> of the cycle (human-out-of-the-loop<\/strong>).<\/p>\n\n\n\n

But is this really the end of supervision, or is it rather a shift right<\/strong>, much more complex and crucial?<\/p>\n\n\n\n

Why does business want us out of the way?<\/h2>\n\n\n\n

Let’s be clear: business is hungry for speed. An operator who has to click \u201capprove\u201d is seen as a slow, expensive, and not always available bottleneck. In a world that thinks in milliseconds, human intervention risks looking like a sloth awkwardly clinging to a branch during a Formula 1 race. That’s why managers and CTOs are pushing to remove people from the digital gears. It’s not out of malice, but “efficiency”.<\/p>\n\n\n\n

This shift redefines the three classic roles of supervision, transforming them radically. In the human-in-the-loop<\/strong> model, the operator acts like a toll collector on a highway: a mandatory and safe passage, which nevertheless slows down the flow, accumulating traffic.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

In the human-on-the-loop<\/strong> model, we shift to the role of an air traffic controller. Removed from the direct flow of action, the human finds himself alone, sitting in front of a silent monitor with a cup of coffee slowly getting cold. His job is no longer to do, but to passively watch abstract trajectories on the display, with the sole and frightening power of hitting a big red button in the event of a collision. It is a grueling loneliness, in which the mind oscillates between the flat boredom of incomprehensible data and sudden spikes of pure anxiety, while progressively losing the real meaning of what is being controlled.<\/p>\n\n\n\n

Finally, in the human-out-of-the-loop<\/strong> scenario, the human limits himself to designing the highway and then letting it operate autonomously. It is maximum efficiency combined with maximum risk: you find yourself hoping that the automated systems work properly, while getting distracted by the latest episode of a TV series, metabolizing a dangerous delegation of responsibility.<\/p>\n\n\n\n

This transition, however, exposes us to a subtle risk. It is often taken for granted that combining humans and machines is the perfect formula to maximize operations while reducing risks, but the reality is quite different. When an automated system collapses, control is suddenly handed back to the operator at the worst possible moment: in the midst of complete chaos, and without them having a deep understanding of the problem’s dynamics. In this way, the human is downgraded to a mere “responsibility collector”, ready to absorb the legal, economic, and psychological blame for a disaster they had neither the tools nor the physical time to prevent.<\/p>\n\n\n\n

Total automation<\/h2>\n\n\n\n

We are not talking about science fiction, but real scenarios in sectors we manage daily.<\/p>\n\n\n\n

Software engineering: our new colleague is an AI agent<\/h3>\n\n\n\n

Our desk mate is no longer the experienced and somewhat nostalgic colleague, but Claudio, Gippi, Quenno, or Deep: autonomous agents that draw their instructions from simple Markdown files.<\/p>\n\n\n\n

In this context, the agent can act as a programmer, tester, or documentation writer depending on the need. We find ourselves surrounded by virtual agents that actively collaborate, ask questions, perform tasks, and write code the very instant we start conceiving it. They work tirelessly, even at night while we try to relax. These systems plan, self-correct, duplicate, and coordinate completely autonomously. For us programmers, daily activity is shifting from manually writing code to orchestrating fleets of agents in the cloud and validating their action plans.<\/p>\n\n\n\n

The case of Nubank is emblematic in this sense: a complex ETL migration of 6 million lines of code. On paper, it was the classic “suicide mission”: an estimated 18 months of work for a thousand developers and endless nights of cold pizza and coffee. AI wrapped it up in just a few weeks, spending a twentieth of the budget. A monstrous efficiency that, while freeing us from a tedious manual nightmare, leaves us with a staggering realization: our role is changing, and we must come to terms with it.<\/p>\n\n\n\n

Try to put yourself in the shoes of the Project Manager who drafted that migration estimate. With 18 months of locked budget, they were already looking forward to a dream family vacation on a Thai island, imagining sipping an iced Cha Yen and enjoying a piping hot Pad Thai on a beach in Southeast Asia. Instead, they watched their planning crumble under their feet in less than a month, thanks to a fleet of cloud agents operating even at night. Now they find themselves sitting in the office redesigning all business objectives, trying to figure out how to sell and manage a job where people no longer lay the bricks, but only have to validate the design of the synthetic architect.<\/p>\n\n\n\n

Source: Nubank, the migration as told by Devin<\/a>.<\/em><\/p>\n\n\n\n

AI training: now AI judges itself<\/h3>\n\n\n\n

Many will remember the complexity of Reinforcement Learning from Human Feedback (RLHF): a multitude of people hired to label what was deemed correct or incorrect. A slow and expensive process that almost comically recalled the old school lists written on the blackboard by the classmate “Luigi” of the day, who decided who was good or bad based purely on his personal liking at the moment. This arbitrary approach proved poorly scalable: people are expensive and risk losing their minds after the hundredth evaluation in front of the PC. The industry has therefore shifted decisively towards AI Feedback: machines train, correct, and evaluate each other in a continuous cycle. An undeniably ultra-fast and cheap system, but one where the “student” AI only learns to please the abstract rules of the “teacher” AI, just like the teacher’s pet who brings an apple to the desk without having actually understood the lesson.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Modern agentic frameworks also follow this pattern: systems where groups of agents analyze each other, correcting and evolving autonomously. Anthropic’s Constitutional AI is built on exactly this principle: systems that correct themselves by adhering to a specific constitution of predefined rules. With a detail that changes everything: for now, we are still the ones writing that constitution. We delegate the execution of the judgment to the machines, not the choice of values on which that judgment is based.<\/p>\n\n\n\n

Moderation and support<\/h3>\n\n\n\n

In content moderation and customer support, AI is also taking a dominant role. Automated triage systems now filter the majority of contacts, delegating only the most ambiguous or sensitive cases to human operators.<\/p>\n\n\n\n

Klarna pushed this concept to the extreme, replacing hundreds of agents with a virtual assistant. Although initial metrics showed a drastic reduction in response times and an unprecedented volume of conversations handled, the lack of empathy and flexibility generated strong customer dissatisfaction and an unexpected increase in operational recovery costs. The lesson is clear: extreme automation offers extraordinary efficiency, but when complexity, judgment, and empathy come into play, human presence remains indispensable<\/strong>.<\/p>\n\n\n\n

Source: Klarna backtracks and starts hiring again: Corriere della Sera<\/a>.<\/em><\/p>\n\n\n\n

Despite temporary setbacks, the direction set by the industry is clear. Even if we must accept qualitative compromises today, the progressive sophistication of models will lead to the almost complete exclusion of humans from direct operational flows. While on one hand this will free us from repetitive and alienating tasks, on the other hand it will bring us closer to a scenario where our contribution is confined to high-level supervision or managing only critical exceptions.<\/p>\n\n\n\n

The pathologies of autonomy: when the machine bites its own tail<\/h2>\n\n\n\n

Wild enthusiasm for digital autonomy is beginning to clash with unforeseen behaviors. Fully automated systems lacking a real external anchor tend to develop genuine operational pathologies.<\/p>\n\n\n\n

The collapse of synthetic critical thinking<\/h3>\n\n\n\n

Entrusting the judgment of an AI to another AI generates a logical short-circuit. The evaluated model quickly learns to optimize its answers to please its synthetic evaluator, rather than being genuinely useful to the real user. If in the past a student brought an apple to the teacher to win her over, today AIs are literally trained to satisfy formal parameters. We are training models to pass evaluation tests with flying colors, while neglecting a true understanding of the subjects.<\/p>\n\n\n\n

The result is a massive production of long, ceremonious texts lacking real informative value: a false synthetic consensus that masks the total absence of critical thinking and diversity. Phrases like \u201cYou had a fantastic idea, what a brilliant intuition\u201d<\/em>, repeated like a mantra at the beginning of every response, do not belong to human language. Machines adopt them exclusively because they know that this condescending tone satisfies the evaluation criteria of the judging algorithm. This feedback loop ends up rewarding mediocrity, penalizing originality, and flattening the entire information ecosystem.<\/p>\n\n\n\n

Europe puts a patch on it (or maybe not?)<\/h2>\n\n\n\n

While the industry pursues the goal of fully automated processes, the European legislator has introduced a sharp halt with the approval of the EU AI Act. Article 14 requires that, for systems considered high-risk (such as healthcare, transport, or finance), a final human control must always be guaranteed, with the possibility of promptly interrupting the system’s operations.<\/p>\n\n\n\n

The requirements defined by the law seem almost designed by user experience experts: diagnostic tools to map the system’s limits, interfaces designed to mitigate the tendency toward automation bias by prompting operator attention, explainability criteria to understand the model’s logical pathways, override possibilities to modify or cancel decisions, an emergency button, and cross-biometric controls.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

On paper it’s fantastic, but the reality is likely to be quite different. Are we not sliding into the same tunnel as cookie banners, where nobody reads anything anymore and everyone just clicks repeatedly so as not to waste time?<\/p>\n\n\n\n

Supervision could be reduced to a formal and automatic ratification so as not to compromise production speed. Furthermore, the obligation to keep a human supervisor in the loop risks setting up the latter as the ideal legal shield for software producers, who will bear the responsibility for not having intervened in time, even in the absence of the physical time or visibility necessary to understand the algorithm’s error.<\/p>\n\n\n\n

Supervising complex software generated by AI means looking at every line, every test, every cycle, and having a holistic vision of it: a task that requires in-depth technical skills, a profound understanding of the operational context, and the ability to anticipate the consequences of every decision; a task beautiful on paper, but practically impossible, unless you were injected with Compound V as a child without suffering irreparable psychic setbacks.<\/p>\n\n\n\n

Conclusions: we are not obsolete, we have become indispensable<\/h2>\n\n\n\n

Are we destined to become the dinosaurs of the digital age? The answer is no, but our work is undoubtedly undergoing a profound metamorphosis.<\/p>\n\n\n\n

We are no longer the digital bricklayers busy laying one line of code after another. We are becoming the architects<\/strong> of these systems: we don’t lay the bricks anymore; instead, we design the structure, set the regulatory boundaries, and define the operational principles within which fleets of autonomous agents can move. This is the world of Harness Engineering.<\/p>\n\n\n\n

And just as every architect is answerable for the stability of a building long after the bricklayers have left, we are left with the final signature<\/strong>: the ultimate instance called upon to untangle the ethical and liability complexities that no machine can govern alone.<\/p>\n\n\n\n

The limitations that emerged from early attempts at total automation offer a valuable lesson: completely excluding human control generates fragile, rigid, and ultimately very expensive systems. It is no coincidence that in sectors where errors are paid for in human lives, such as aviation and medicine, humans have never really left the loop: they remain there due to liability regimes, not nostalgia. The EU AI Act establishes this limit at the regulatory level, reminding us that the future does not lie in a conflict between humans and machines, nor in the exclusion of humans from the loop. The key to success will be the integration of interfaces and workflows capable of governing the operational speed of AI without ever abdicating our professional intelligence and responsibility.<\/p>\n\n\n\n

Our role is far from over: it has simply become much more stimulating, complex, and necessary. We will make the difference by refusing to just suffer the shift right, and instead claiming it: demanding to understand the systems we sign, refusing to be downgraded to mere responsibility collectors, keeping our hand on the button only when we really know what we are stopping. That is, as long as we don’t let ourselves be worn down by colorful interfaces full of red buttons, inviting us to click without thinking too much.<\/p>\n","protected":false},"excerpt":{"rendered":"

Spoiler: Business is gently escorting us out of the door of AI processes. But maybe it’s not the end of our job\u2014rather, the beginning of something much more interesting. Do you remember when, to approve any change, you had to wait for the manager’s signature on a mimeographed document? We need someone responsible. This was… Read more<\/a><\/p>\n","protected":false},"author":246,"featured_media":35928,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[46],"tags":[],"collections":[],"class_list":{"0":"post-35934","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-ml","8":"entry"},"yoast_head":"\nIs the human the bug or the feature of AI development? - Codemotion Magazine<\/title>\n<meta name=\"description\" content=\"Are we destined to become the dinosaurs of the digital age? 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