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Why technology workers aren’t going anywhere even though you wished AI would make them all lose their jobs

It almost happened in 2015 with the emergence of “Low Code / No Code”. Back then, digital product and software project managers believed that there now existed an abstraction layer powerful enough with Salesforce, Shopify, Wix, Bubble and all the No Code and Integration platforms out there, that they would finally be able to reduce their dependency on software programmers and IT engineers. The resentment toward the players of the Tech Industry was getting to a point where actual attempts were made to make some of them disappear. As soon as a few of those attempts became successful and needed to scale for critical mass adoption, guess who they had to call? Coders, to customize, migrate, secure, automate, monitor, stabilize, integrate, maintain, fix, localize and scale their business models born in the hands of technology platforms that owned their processes, their business rules, their software, their documentation, their roadmap AND more importantly, their DATA. The dream was there for a while for the business people not having to “deal” with coders but the technology was not there quite yet. Everyone had to patiently retreat in their corners and learn again to tolerate each other, communicate with each other, understand each other and sometimes perform and achieve together leading to the success of their ventures.

When this happened, as a CTO, I felt compelled to understand what was the source of such resentment. So strong that it translated into some colleagues wishing they didn’t have to work with certain of the highly experienced, highly knowledgeable teammates we barely managed to recruit with sign-on bonuses and stock options. It’s as if programmers ever suggested that they didn’t need Product people or Marketing people in their company or that Finance was seeing them too much like a cost center not enough like a value-creation area of the business. Technologists would never suggest such things, would they?

It turns out you can count with very few fingers how successful companies have witnessed flawless understanding and collaboration with their technology team. And the main reasons slowly building up into resentment are common to many business endeavors:

1. Technology knows things the business doesn’t understand and programmers use this as a competitive edge and it creates the semblance of an elite that makes business people feel belittled.
2. Business has a hard time dealing with the fact that the people who truly have the last say in the realization of their ideas or the meeting of their needs are technology doers. Business feels overridden.
3. Technology not only understands what the things they built are made for but they also understand what they can do. It leads them to being tempted to sell what they did more than the one time as “expertise”. Business feels ripped off.
4. Business needs software and platforms that fit their processes like a glove. Technology wishes the needs were simpler so the lines were more straight so they can move on faster to new things. Business feels unheard and neglected.
5. Business understands that hitting budgets and deadlines is key to reaching product-market fit, launching successful initiatives and turning a profit. Technology becomes lazy being funded without the need to themselves show payoff and return over investment. Business feels Tech is not grateful enough for funding their activity. Business wishes tech would be more grateful.
6. Technology does things nobody else in the company can do. They fail at expressing complexity, they struggle at sizing effort, they communicate poorly about their challenges and what it takes to overcome them. Business feels kept in the Dark and dependent.
7. Business people too often feel at the mercy of their Tech team when it comes to discussing how things will be implemented or what tools and partners will be used. Business feels like working around their Tech team on projects they really care about would allow them to stay in control of their projects and no tech team likes to be denied a say in the implementation of new technology that they will be asked to integrate with anyway.

When I understood that, I realized that the Tech industry that had made all my life dreams possible was heading in the wrong direction for the next generations. It led to the foundation of CodeBoxx in 2018 where an accelerated technology program is proposed not only to teach the hard skills and latest tools of technology now powered by Artificial Intelligence, but more importantly to teach the soft skills and values for the humans that are meant to remain behind Artificial Intelligence. And if AI has now reached a maturity level where coding is no longer a barrier to entry for entrepreneurs and technology can now be generated using natural language and leveraged by virtually everyone to offer quality software, the values and mindset of the humans behind AI expressing requirements and constraints now need to fit the upcoming business and even societal changes coming our way.

That is how the profession of software programmer or IT engineer is now finally evolving into the Software Technologist role that the industry has always wished for. The business never really wanted for the Tech team to disappear. They wanted them to be better, more in tune with their needs, ambitions and aspirations. And now that developers are no longer stuck in writing code 60% of the time, they can evolve in focusing on things that truly matter to their colleagues. As software technologists get to user-experience fit faster, Sales, Product, Marketing and Finance will reach product-market fit faster and more cost-efficiently.

It’s useless to spell doom over the Tech industry by saying that Generative AI is revolutionizing coding and rendering software engineers obsolete. Because this revolution is not the end of the discipline. We already know that because, since I have been in Technology since 1985, It’s already the 5th big reset our professions are going through that I am witnessing.

First there was the Desktop Revolution. Bill Gates, in his latest memoir, refers to it as the “A Computer on every Desk” vision that led to believing software on top of democratized computers would empower individuals within households and businesses. It is this very revolution that made a computer land in my own home, connected it to my TV and recorded data from my basic programs on magnetic tape. More importantly it made me and an entire generation of kids understand that a career in Tech was something one could envision. The languages that were available back then were made for terminals linked to centralized processing centers. We had to learn to code for CPUs and had memory as a constraint. We learned to code, compile, package, install and distribute software. The first revolution expanded through peripherals that required code of their own to operate. The shift in mindset from terminal to desktop was a revolution that rendered a whole set of hardware, languages and protocols obsolete. The industry players started all over to produce software for microcomputers .

Second there was the Web Revolution of the Internet that basically gave back to your computers and laptops the status of simple terminal as everything started happening inside Web Browsers calling remote servers. Once again, the tools and languages we learned got wiped out and we had to learn new frameworks and we had to choose between those who believed in repecting intellectual property protected within licensed software or those who believed that source code should be open and shared so everyone can have access to the best way to solve problems with code. Compiled languages were no longer enough for us all to master and we had to learn javascript and markup languages. We had to learn how to keep those servers always on and monitored to deliver flawless uptimes and fast compute for the masses. We had to learn it all over again to produce software for the Web and then again for Web 2.0 unleashed by an infinite supply of bandwidth, compute and storage. We had to invent, standardize and learn the ways for platforms to not only talk to humans, but now also talk to other platforms allowing for data, events, truth and facts to travel faster than the speed of thought.

Third then there was the Revolution of the Cloud that took our physical serThird then there was the Revolution of the Cloud that took our physical servers away from our LAN Rooms and our Shared Locations, forcing on us the mastery of a whole new IT operations layer. Compute as a commodity becoming the norm, we had to invent new tools and specialties such as Development Operations (DevOps) to allow growing teams of software engineers to safely develop and release their code in front of customers. We had to re-learn how to collaboratively build software to meet tighter release schedules and flawless operations and 100% uptimes became a standard and no longer an accomplishment. We had to learn how to do parallel programming, first for multi-core CPUs and then reinvent it again, this time for GPUs with languages like CUDA. The abstraction of the infrastructure in the Public Cloud had us start all over once again and acquire a whole set of new skills, scripts and languages and build new tools to standardize this new layer that unleashed a whole new level of potential for businesses and it enabled or accelerated their digital transformation.

Fourth, came the Mobile Revolution that truly started in 2008 where the launch of the iPhone allowed everyone to aspire to be augmented by a smartphone. The combination of touch screens and high-speed wireless internet unleashed a whole new realm of possibilities through mobility. This time again, software engineers had to re-learn how to code software so all their platform capabilities were now exposed in a manner adaptive to a growing variety of form factors. The emergence of Native Apps downloadable and installable from App Stores came with the need to master yet another whole new set of programming languages and brought back modern versions of antiquated ones that past revolutions had forced us to leave behind. At the time of writing of this post, many people from the industry established 60 as the number of distinct software frameworks available to use in a modern technology setting. Some others were able to reference 80 different frameworks with a significant following, code in production and a toolset to support them. The sheer quantity of languages and frameworks had already started to get out of hand. The Mobile era became the final blow making it impossible for individuals to master them all. With languages evolving into frameworks evolving into stacks evolving into complete ecosystems, increased demand for skilled software developers became unsustainable. But in front of such adversity, the technology industry itself found its own way through a wild push toward a long-neglected but now promising field of research: Artificial Intelligence and particularly neural networks powering machine learning.

And then came Artificial Intelligence maturing into its Generative form. That revolution is and will remain the fastest and most significant revolution of our industry and it will be know as “The Great Reset“. It will be unavoidably so because it is making the center of power shift from code to requirements. Through the use of the latest Large language Models and Generative AI, the Software Engineer is freed from the need to master the syntax of all languages and all foundational frameworks that compose a software or a platform. Individuals in charge of developing software now have more bandwidth to dedicate to the actual operationalization of the code. Reaching User Experience fit is no longer a secondary task that kicks in once all the code has been written and tested. As of today, 4 Large Language Models have already and repeatedly confirmed the ability to produce higher-quality code than any senior software engineer ever could, no matter how many hours of experience this senior engineer might have possibly accumulated. It will never write, review, debug and test as fast as coding agents do today. Moreover, these agents don’t sleep, don’t get bored, have no preferences for language or teammates and they will never push back nor complain when asked to improve or re-do a portion of their work. With the writing of code now handled in perfect alignment with standards and guidelines, focused can be turned to the requirements:

1. Clarity
2. Specificity
3. Context
4. Correctness Framing
5. Completeness
6. Testability
7. Level of determinism
8. Avoidance of Overload

The quest for quality software has now shifted its attention from quality of code to quality of requirements. The fact that they are expressed in Natural Language allows for all business and technology actors to now have a common frame of reference to structure their collaboration. The translation into code by humans is no longer a risk factor in the production of Quality Software. That is the True Revolution that Generative AI has enabled for the Technology Industry by lowering the barrier to entry to always leveraging the right languages and frameworks for the right use in any given situation. No business is ever going to be at the mercy of a small group of elitist technologists imposing their ways, their stack and their architecture simply because when they hold a hammer, everything needs to look like a nail..

The journey through these technological epochs reveals a consistent pattern: innovation sparks disruption, redefines roles, and demands new skills, yet throughout all these revolutions, the human element of technology endured. The ‘Great Reset’ of Generative AI is not an end, but a powerful catalyst. It liberates software professionals from the drudgery of syntax, allowing them to truly become ‘Software Technologists’ – adept at translating human needs into digital solutions.

This shift isn’t about AI replacing humans, but about empowering humans to focus on what truly matters: clear communication, empathetic understanding, and the precise articulation of requirements. When business and technology finally speak the same language – the language of clear objectives and shared aspirations – that’s when the magic happens. The future of technology isn’t just about faster code; it’s about better collaboration, more profound innovation, and ultimately, building a world where human ingenuity, amplified by AI, solves problems with unprecedented clarity and speed. Let’s embrace this evolution, not as a threat, but as an unparalleled opportunity to forge stronger, more effective partnerships in the digital age.