If you close your eyes and imagine a programmer, you probably picture the Hollywood cliché: a hoodie-clad person in a garage or dirty room littered with pizza boxes, frantically typing green lines on a black screen while drinking energy drinks. That romantic image of the ‘lonely code craftsman’ is about to become a museum piece, right next to the fax machine and floppy disks.
As we approach the end of 2025, it is clear that we are witnessing a structural transformation in technology departments that goes far beyond the simple adoption of new tools. For marketing and business development leaders, understanding this change is crucial: a company’s technological production capacity is no longer measured by hours of manual programming, but by its ability to orchestrate artificial intelligence.
The Great Migration: From Translators to Editors.
To understand the magnitude of what is happening with IT teams, we can draw a parallel with what has been happening in content teams. A few years ago, writing a blog post meant facing a blank page. Today, many writers use AI to generate structures, ideas, or drafts, and then put their talent to work editing, curating, and adding a human touch.
Something identical has happened in programming, but at a slightly faster pace.
A developer used to spend 80% of their time on “syntax”. That is, how to write a specific function, which library to use, or how to fix a code error. It was like having a great idea for a campaign, but taking three days to translate it into German because you had to look up every word in the dictionary. Today, the focus has shifted radically towards the ‘what’ and the ‘why’.
Generative AI acts as that perfect simultaneous translator. Developers no longer have to worry so much about how to write the function in Python or JavaScript; now they tell the AI what they need that function to do.
Conventional tasks have undergone a dramatic shift:
- Documentation and Knowledge Transfer: Previously, documenting software (explaining how it works for future developers) was a costly and often neglected task, which led to ‘technical debt’. Today, AI analyses code and generates accurate, up-to-date technical documentation in real time. This reduces new employee onboarding times and ensures business continuity.
- Debugging and Maintenance: Identifying errors used to be an exhaustive manual search process. Today’s tools not only locate the anomaly, but also suggest the correction and explain the root cause, transforming hours of downtime into minutes of resolution.
- Quality Assurance (QA): Instead of writing manual tests for every possible scenario, teams now use AI to predict complex usage scenarios and generate stress tests that a human might have overlooked, increasing the robustness of the product that reaches the end customer.
2025: The Year the Machine Learned to ‘Reason’
The year 2024 was marked by the generative capacity of language models (their ability to produce text or code), while 2025 has established itself as the year of advanced reasoning.
Until recently, there was some mistrust of AI due to “hallucinations” (incorrect answers presented with confidence). However, the implementation of models based on “Chain of Thought” this year has significantly mitigated this risk. Unlike their predecessors, which operated probabilistically and immediately, the 2025 models have the ability to ‘pause’ and evaluate multiple logical paths before offering a solution.
This has transformed the dynamics of human-machine collaboration within technology teams:
- Architectural Decision Making: Senior developers no longer use AI just to write code, but to question the system architecture. They can pose a scalability problem, and the AI will mentally simulate different database strategies, evaluating pros and cons with almost human-like judgement.
- Risk Reduction: With its reasoning capabilities, AI can audit code for security vulnerabilities or business logic flaws before the software goes into production.
- Staggered Mentoring: For junior profiles, AI now acts as a technical mentor available 24/7 that not only provides the answer, but also explains the reasoning behind it, accelerating the team’s learning curve and professionalisation.
2026: What does the crystal ball hold in store for us?
If you think this is happening fast, wait and see, because 2026 promises to be the year of the ‘Autonomous Agency’.
Currently, interaction is passive: humans request, machines execute. By 2026, technology teams are expected to manage agents with assigned objectives. Instead of requesting lines of code, a technical leader will be able to assign a business goal to an AI agent, such as: ‘Audit the performance of the web payment process, identify bottlenecks, and propose and implement an optimisation to reduce latency by 20%.’
What is expected by 2026 is that technology teams will begin to manage AI agents. Imagine small, autonomous virtual employees.
- Instead of asking AI to write code, the developer will tell an agent: ‘Your mission today is to check that all the forms on the website are working properly. If you find one that’s broken, fix it and let me know.’
- The agent will not ask you how to do it. It will go, browse the website, test the forms, detect the fault, write the code to fix it, perform security tests, and present the result to the human for final approval.
Conclusion: More Human Than Ever
The most relevant conclusion for the business ecosystem is that the technical barrier is diminishing.
This means that developers can stop focusing exclusively on interaction with the machine and concentrate on interaction with the business. We are witnessing the birth of a technological profile that is much more empathetic to the needs of marketing, sales and user experience. AI is accompanying the technical team, elevating them to a more strategic position, allowing them to move away from being ‘incident solvers’ to become true architects of business value.