IThe Silicon Sage at Sixty: Navigating the Digital Paradox of Age in Tech
Designed By Rob Tyrie
As I powered up my pc this morning – a ritual I’ve performed countless times over four decades – the familiar startup chime triggered an unexpected reflection. This year marks my sixtieth trip around the sun, and in an industry where "Senior Developer", “Product VP" or “Business IT Architect” often means anyone over 35, I find myself in a peculiar position: a digital elder in an ecosystem that simultaneously venerates experience and worships at the altar of youth.
The technology landscape I survey today bears little resemblance to the one I entered in the 1980s, when we debated the merits of Pascal versus C, C++ over C, and "cloud computing" meant a mainframe somewhere in a windowless building was processing our batch jobs. Or it was some data center where TSO was the cloud, and JCL was a mystic power of the DevOps accolytes of the '80s. Yet paradoxically, the fundamental challenges remain unchanged: systems still need to be useful automatons, systems still need to scale, algorithms still need to be implemented data still needs protection, someone still needs to pay the power bill, and despite our best efforts, production servers still choose 3 AM to demonstrate their mortality. Pagers anyone?
The Persistent Paradox of Progress
Today’s software engineering environment presents a fascinating dichotomy. While we’ve evolved from punch cards to containerized microservices, the essential patterns of technology evolution remain remarkably consistent. Modern developers might wrestle with Kubernetes or API orchestrations, but the underlying challenge – managing complex, distributed systems – mirrors the challenges we faced with early client-server architectures. The tools have changed, but the problems persist in their elemental form.
Consider artificial intelligence, the current catalyst of industrial transformation. While the technical implementation has evolved dramatically from expert systems to deep learning, the fundamental challenge remains unchanged: how do we encode human knowledge and decision-making into systematic, reproducible, “automatable” processes? The questions I pondered while programming rule-based systems in LISP and PROLOG three decades ago echo in today’s discussions about large language models and neural networks. (Ed: I had to make up that word automatable... Which is the parallel to manufacturable in software land. A lot of ideas although they’re good ideas just aren’t out automatable available either because of economics or complexity. That doesn’t take away the good idea though.. It just may be in an early signal to the future when system becomes more powerful and can handle more complexity at low cost).
This is a good spot to have a shared definition of what a software system is. So let’s do it with Weiner’s Cybernetics in mind:
A software system is a collection of interconnected programs and processes designed to achieve a specific goal by processing data and producing meaningful results. Drawing from Norbert Wiener's cybernetics, it can be understood as a system that involves inputs (data), processes (how the data is transformed), outputs (results), and feedback loops (mechanisms that refine the system based on results).
In simpler terms, think of a software system like a self-adjusting recipe: ingredients (data) are gathered, a chef (the program) follows steps to transform those ingredients (process), and the dish (output) is tasted. If the flavor isn’t quite right, the chef tweaks the recipe for the next time (feedback).
Most importantly, almost all software systems involve humans—whether to design, operate, interpret, or adjust them—either continuously or at critical stages. The humans in the loop are essential to the software as was discovered in the philosophy and wisdom The Matrix and The Simpsons.
The Advantage of Algorithmic Archaeology
At sixty, one possesses what I call "algorithmic archaeology" – the ability to recognize patterns across technological epochs. This perspective becomes invaluable when evaluating new technologies. Having witnessed multiple cycles of innovation, one develops an intuitive sense for distinguishing genuine paradigm shifts from rebranded solutions to persistent problems.
For instance, today's microservices architecture bears striking similarities to the component-based software engineering principles we discussed in the 1990s. The implementation differs, but the underlying architectural principles – modularity, loose coupling, and service abstraction – remain fundamentally unchanged. This historical context allows senior technologists to contribute unique value in architectural decisions, helping organizations avoid reinventing square wheels.
The Digital Darwinism of Career Evolution
Survival in technology requires continuous adaptation. At 60+, this means maintaining a delicate balance between leveraging accumulated wisdom and remaining open to new paradigms. The key lies not in mastering every new framework or language, but in understanding the fundamental principles they embody.
I've found that the most successful late-career transitions involve positioning oneself as a "technical philosopher" – someone who can bridge the gap between business strategy and technological implementation.
"Geometry, music, and other studies of the same sort are not only noble but necessary. Yet they are merely handmaids to philosophy. The architect, too, must know geometry; otherwise his measures will be inaccurate, and his buildings will lack symmetry. But still, geometry is not architecture; it is only an instrument of the art." Seneca
The architecture, builder or product role, leverage both technical depth and broad historical perspective, making age an asset rather than a liability. This is the part where you do have to see the forest and the trees; and the roots and the leaves. When you come to the table with a group of people with a mix of software and business backgrounds, you do have to build out a conversation, a dialectical dialogue, that goes into a depth that ends up in implementation that will go into production. You literally have to predict a success based on your experience and your assessment of what is possible given the constraints of yesterday and the needs and promises of tomorrow.
The Mentorship Multiplier
Perhaps the most profound opportunity at this career stage is the ability to serve as a mentor. Young developers might outpace us in coding speed or framework familiarity, but they benefit immensely from our battle-tested wisdom about system design, scalability challenges, and the human factors in technology implementation. In training managers to be more senior and to develop executive skills, I’ve often asked them who their best mentors are. If they don’t have a fast answer and name of a human-being that actually exists and that they meet with on a regular basis, who know them and understand them and their path, I make suggestions for them to go find one because I believe you can never be a mentor unless you’ve had a mentor in my opinion. The second element is more subtle and much more important. I ask the senior managers who want to get ahead, if they are mentoring anyone. Sometimes I get a quizzical look because... They are managers of course and say they’re mentoring someone — their direct reports. They have staff. This, of course is not what I mean by mentoring. A Mentor is a teacher and a guide and someone who loves your career as much as you do and wants the best for you. That person is the person in your corner. And that person is willing to give you tough news if you’re not doing well. From time to time your mentor maybe a shoulder to cry on, a coach to give you the tip you needed at the last minute, or maybe they’re just the wind beneath your wings before a big pitch presentation to give you that positive feedback that no one else ever does.
So I asked him who they are mentoring. If they are not mentoring, well that’s my form of mentorship to them to make sure that they’re doing it so they could see how much it helps them. That’s the one of the secret magics of being a mentor- It’s helpful for both the people in conversation. Be a mentor to at least one person and as many as you can handle effectively, and where you can make a difference in their lives by knowing them very well and seeing them as if with an x-ray vision of wisdom.
The Economics of Experience
Contrary to popular narrative, the market value of experienced technologists remains robust, particularly in roles requiring deep system understanding and strategic thinking. Organizations increasingly recognize that while younger developers excel at implementation, senior technologists provide crucial guidance in architecture, risk management, and strategic technology alignment. Put your rates up high, the highest they’ve ever been on an hourly basis, and make sure you stick to it because you got to pay yourself first, and you’ve got to pay your overhead that is there to grow your practice. You have to value your expertise and ability to do more work in less time than others with less risk of do-overs as you look at the future. It’s not a crystal ball. You want to start valuing your expertise It’s probably a good time, if you haven’t already, to do a deep dive into the work of Kahneman and Tversky, and the rest of the people who’ve worked on the psychology of behavior and decision theory in humans. These are Nobel prize winners. And, they can be applied to your situation at 60 as much as they could have been applied to a situation at 20. If you don’t know, System One thinking is fast decisions instantaneous actions and achieving a state of flow immediately on demand because you are in your expertise. Just the one thinking is your beginning mind as well It is the first type of thinking that a human was developed but it never stops being developed and it’s connected to and builds System Two thinking. The science proves that everyone has these two models running simultaneously in their brain is a way of thinking about how we think how cognition works. And the two models in your brain think differently. This is provable under the scan of an fMRI. It’s still just a model l, but it’s pretty useful. It is up to you to build your system too Your deep thinking and deep consideration of your most memorable memories that you can pull up and retrieve and use at a moment’s notice in sub second response time depending on the situation. Younger people or people without a System Two or an immature System 2, marvel at the abilities of someone to assess a situation rapidly make a decision and change the path of a project from failure to at least production deployment and the chance for the project team to have a “day two" phase to fix what’s broken. It’s those quotable quotes, your ability to write rapidly and summarize things, your ability to do deep work and create designs and communication strategies that really manage risk until software is in production. It is a very complex human system that has never been imitated yet, no matter what Some of the early mavens of AI may tell you now. When we talk about AGI we’re really talking about system one and system two available across a plethora of expertises running as interconnected interdependent AI agents. When you mark it all down it comes to just one thing . It’s important.
It’s your System 2 Thinking. People with experience and wisdom who have built their skills over time have a well-developed system two that can be invoked with their system one thinking. System 2 comes from deep learning and deep work. If you can develop this system and communicate about it broadly, and definitely into your network it will make you a more valuable consultant and advisor and… executioner. It’s the reason that you take on fixed price projects and deliver great margin business well above 50% to your practice and give deliverables to your customers that they will be delighted by. You have to get there and you have to keep developing it for the rest of your career and practice. I think the system two memories and systems are limitless in humans, because of their nature.
The Path Forward: Strategic Steps for Digital Sages
For those navigating their sixth decade in technology, here are crucial strategies for maintaining relevance and impact:
1. Position yourself as a technical strategist and advisory rather than just an implementer. Focus on architectural decisions, system design, and technology strategy where experience provides distinct advantages. You are not the one anymore, the team is the one.
2. Develop your role as a bridge between technical teams and business leadership, leveraging your ability to translate between technical and business concerns. This is almost like a transformation and translation capability and you will be aided and abetted by fine-tuned generative AI tools that you must build for your practice.
3. Cultivate expertise in emerging technologies through strategic learning – focus on understanding architectural principles and business implications rather than implementation details. Invent and communicate new methodologies methodologies based on your adoption and adoption of new tools and frameworks in software. Create or do courses. Don’t be a bystander.
4. Build a personal brand as a technology thought leader through writing, speaking, and consulting. Be consistent, and publish weekly. Publishing is production. Even short 300 to 500 word “point of views” are super important that you should be able to create every day. If you are wise design them with pattern languages so you can turn them into prompts. I mean real 0attern Languages per Christopher Alexander.
5. Mentor actively, creating value through knowledge transfer while staying connected to current technical challenges. This will refine your system through thinking more rapidly than any other way. This combines nicely with the reading of books which is a not so secret superpower of successful advisors. Great things down either during or after every meeting along with your thoughts about what went on and what got decided and what to do next. Combining these elements together, is almost as good as writing books but not the same. Be like Franklin. He copied things and wrote them down and distributed them to other people after adding his flair to it.
Essential Reading for Technical Sages and their books:
All good sages need guides and some guides are in books here’s a subset of books that I have found useful in creating software systems and teaching people how awful really works, I guess created to work in production and deliver all those business case promises that the management consultants tend to write in vacuums. There are many more of these types of books available and this particular set is a good beginning point even if you’d read the first three chapters of each.
This reading list embodies our essay’s central message: that success at sixty in technology comes not from chasing every new trend, but from developing deeper insight into the patterns and principles that underlie all technical innovation. Each book offers unique perspectives on how to leverage experience while staying relevant and impactful in an ever-evolving technical landscape.
"The Phoenix Project" by Gene Kim, Kevin Behr, and George Spafford - This novel brilliantly illustrates how the patterns of manufacturing and systems thinking apply to modern IT operations. It particularly resonates with our discussion of how fundamental patterns in technology persist across decades, just manifesting in new forms. The book's exploration of DevOps principles mirrors many of the organizational challenges we've witnessed evolve from the mainframe era to modern cloud operations.
"Stealing Fire" by Steven Kotler and Jamie Wheal - While not explicitly a technology book, this exploration of peak performance and innovation directly applies to maintaining creative vitality in late-career technology roles. The authors' insights into flow states and optimal performance complement our discussion of how senior technologists can continue to add unique value through strategic thinking and pattern recognition.
"Thinking, Fast and Slow" by Daniel Kahneman - Essential reading for understanding how cognitive biases affect technical decision-making. Kahneman's work is particularly relevant to our discussion of "algorithmic archaeology" - helping us understand when our past experiences provide valuable insight and when they might blind us to new possibilities. The book's exploration of System 1 and System 2 thinking provides a framework for balancing intuitive pattern matching with deliberate analytical thinking in technical leadership.
"The Checklist Manifesto" by Atul Gawande - Gawande's insights into managing complexity through simple systems perfectly align with our discussion of how fundamental patterns persist in technology. The book's emphasis on combining expert judgment with systematic approaches mirrors the unique value proposition of senior technologists who can blend deep experience with structured methodology.
"Obviously Awesome" by April Dunford - This book's focus on positioning dovetails perfectly with our discussion of how senior technologists can position themselves for maximum impact. Dunford's framework for finding and articulating unique value propositions provides practical guidance for implementing our essay's advice about transitioning from pure technical roles to technical strategy and leadership positions.
These texts collectively form a curriculum for what we might call "technical wisdom" - the ability to see both the forest and the trees in technology evolution. Each book approaches different aspects of our core thesis: that success in late-career technology roles comes from combining deep technical understanding with strategic thinking, pattern recognition, and effective positioning of expertise. This was not the intent of April’s book as it was created but it can be applied to this challenge of creating a new kind of company and a new personal brand based on your positioning. This works for B2B companies and it also works for a solo entrepreneurs any bit of common sense and thinking to apply to her rapid well designed, simple framework. Positioning, after all is said and done, a battle for minds, as it was divined by those marketing geniuses Reis and Trout, who spoke not from the White Towers of academia but from the school of hars projects and analysis of what makes things successful in real marketplaces.
When read together, these works provide both the theoretical foundation and practical guidance for implementing the career strategies outlined in our discussion. They help senior technologists develop what we might call "meta-expertise" - not just knowledge of specific technologies, but understanding of how technical knowledge itself evolves and can be best applied across different contexts and stages of one's career.
These are ones I’m reading next because I never stop learning.
“The Innovation Stack" by Jim McKelvey - For understanding how technical innovation creates lasting business value.
"Team Topologies" by Matthew Skelton and Manuel Pais - Modern perspectives on technical organization that benefit from experienced interpretation.
"Working in Public" by Nadia Eghbal - Essential insights into how modern software development communities function.
"The Manager's Path" by Camille Fournier - Valuable for transitioning from technical expert to technical leader.
"Design Patterns" by The Gang of Four - Because some principles truly are timeless.
As I reflect on my journey, I'm reminded of Grace Hopper's wisdom about the most dangerous phrase in language being "we've always done it this way." At sixty, our role is not to be keepers of the old ways, but interpreters of the eternal patterns in technology's evolution. We stand at a unique vantage point, able to see both the cycles of innovation and the constants that underlie them.
In an industry that often mistakes novelty for innovation, the perspective of sixty years offers something invaluable: the wisdom to distinguish between truly transformative changes and the eternal principles that govern how humans interact with technology. This wisdom, combined with continued curiosity and adaptability, ensures that our seventh decade in technology can be as impactful as our first.
Rob Tyrie a consultant and advisor who created and runs a practice called Ironstone Advisory which helps customers get new technology software applications into production for use by their employees, their customers and other stakeholders. When he’s not writing, he’s reading in that dramatically good metaphor of a “devil’s embrace”. He’s focused on maximizing the value from data, compute, algorithms, and energy of course because everything begins and ends with energy. You can be found online anywhere as “@robtyrie”, And when he’s not in his land yacht cruising and exploring with friends, Is digitally trying to create and predict his future. Stay tuned for upgrades.🧙♂️🔮
