“The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.”
— Marcel Proust, In Search of Lost Time
It’s the most wonderful time of the year! The whizzing merry-go-round of striving enterprise slows down for a decently-cooked meal, a holiday movie, and some oh-so-precious downtime to reflect and consider the state of affairs in mainframe modernization.
What? Not your first choice of subject during the hols? Luckily, I’ve done it for you! Grab a sherry and settle in.
Mainframe modernization is now au courant. It’s the eccentric uncle at turkey dinner whose, er, unusual dress sense is now the buzz of the town. It reflects a grudging acknowledgement that, despite their reliability and sturdiness, mainframes do in many (many) cases represent meaningful constraints on an organization’s ability to grow and adapt. Consider:

The idea of modernization as a competency, rather than a project, is finally taking hold. It’s not yet as fashionable as “mainframe modernization,” but you can see the growing chatter around “continuous modernization.”

Most new efforts keep circling paths we’ve already explored... and discarded. We’ve noted (over and over again) that translating code isn’t the cause of modernization failures, it’s the testing and integration uncertainty that happens afterwards. The problem isn’t a lack of AI, the problem is the conventional codegen-first, fix-later workflow.
Can AI speed things up? Absolutely, but often with deal-breaking tradeoffs. We’ve used LLMs to help with discovery, only to find their determined non-determinism a roadblock. We’ve tried pairing AIs to see if they write better code (not really).
Now AI agents are the new black — black boxes, that is — one each for analysis, documentation, codegen, and so on. We’re experimenting with them, too, but suspect that discrete agents can’t be coupled together in reliably predictable ways, when predictability is precisely what you need. (More on AI agents next year.)
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All that said, we’re seeing another trend: hope.
There’s renewed hope in solving this decades-old problem in less painful ways. And while there’s no silver bullet, we’ve been applying our test-driven modernization approach to real workloads, such as:
Workload by workload, we’re building proof that our approach really is better, safer, and faster: one that delivers on the promise of continuous modernization while minimizing new technical debt.
So yes, Virginia, there is a way forward. The real miracle this season is taking something that people fear and loathe and turning it into something routine, even joyful.
If you’ve ever had to take heroic measures to keep things running during the end-of-year craziness, you might enjoy this skit we put together, Holiday Surge.
We’ve wrapped up our On a Limb podcast, not just for the year, but for good. It was fun exploring the elements of risk, and how hard it is to orient around preventing risks vs reacting to crises. Next year, we’re planning on diving a bit deeper into the technological aspects of mainframe modernization — it might be a podcast, or it might not be.
There’s still time to find that perfect gift! We’re pleased to share our last-minute Gift Guide for the Panicked, especially curated for the people who have had to listen to endless takes on AI and need a break.
And last but certainly not least, Happy Holidays from all of us at Mechanical Orchard!
Curious to learn more? Say hello@mechanical-orchard.com.
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*Issue first published on December 18th, 2025. View all newsletters here
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