I became a Software Architect after over a decade of experience as a Software Engineer, developing code in multiple languages and on multiple tech stacks, from embedded to mobile to SaaS. I understand the nuts and bolts of programmatic code, and even though I’m not writing code anymore myself, I rely on my software development background both for making high level decisions and for delving into the details when necessary. If as tech leaders we don’t ensure that we gain equivalent knowledge and hands-on experience in the field of GenAI, we won’t be able to lead the architecture of modern systems.
In other words — I realized that I cannot be a good Software Architect, without knowing GenAI. The same way I can’t be a good Software Architect if I don’t understand topics such as algorithms, complexity, scaling; architectures such as client-server, SaaS, relational and non-relational data bases; and other computer science foundations.
GenAI has become foundational to computer engineering. GenAI is no longer a niche sub-domain that can be abstracted away and left to Subject Matter Experts. GenAI means new paradigms and new ways of thinking about software architecture and design. And I don’t think any Software Architect or Tech Leader can reliably make decisions without having this knowledge.
It could be that the products and projects you lead will remain AI free. GenAI is not a silver bullet, and we need to ensure we don’t replace straightforward automation with AI when it’s not needed and even detrimental. All the same, we need to be able to at least assess this decision knowledgeably, every time we face it.
I’m going to end with some positive news for Software Architects — yes we all have to ramp-up and learn AI — but once we do, we’re needed!
As GenAI based tools become ever more complex, data science and AI expertise is not going to be enough — we need to architect and design these systems taking into account all those other factors we’ve been focused on until now — scale, performance, maintainability, good design and composability — there’s a lot that we can contribute.
But first we need to ensure we learn the new paradigms as GenAI transforms computer engineering — and make sure we’re equipped to continue to be technical decision makers in this new world.