When people ask how AI will shape the future of software, a lot of articles answer with the most intuitive points, heavily focusing on speed: building fast, reducing cost, and shipping fast. That's all good. But what happens after that?
I want to share a few counterintuitive thoughts that came to my mind—angles I haven't seen anyone else talking about yet.
Topic 1: Eternal Software & The Death of the "Software Graveyard"
Historically, one of the most brutal crises facing tech companies was entering the "Software Graveyard" (Software Obsolescence). Before the era of AI, a company’s decision to abandon a legacy product or stop supporting it rarely meant the product itself was bad. It simply meant that the financial and human cost of keeping it alive was absolutely terrifying.
Software used to die because its surrounding ecosystem evolved—operating systems upgraded (iOS, Android), browsers shifted, and security standards advanced. Companies were forced to dedicate entire engineering teams just to chase these external changes, leading to massive technical debt.
AI completely breaks this cycle. We are moving from a culture of "disposable software" to an era of "sustainable, cumulative engineering."
1. The Autonomous Evolution (Automated Compatibility)
Instead of assigning a team to manually refactor code for every new OS update, AI Agents will autonomously monitor the external tech landscape. They will modify, update, and rigorously test the codebase to ensure seamless compatibility—requiring zero draining human intervention.
As a direct result, the concept of "rewriting the system from scratch" will gradually die. The software a company builds today will remain a living, breathing asset for 20 or 30 years without ever feeling "old." AI will continuously inject modern architectural standards into its genetic technical makeup.
2. The Rise of Independent AI Code Auditors
When a codebase lives for decades and undergoes thousands of automated AI refactoring cycles, a critical question emerges: "Who guarantees that continuous automated updates haven't introduced hidden security vulnerabilities over the years?"
To solve this, companies will establish entire departments dedicated to AI Code Governance. Their core responsibility won’t be writing code, but deploying highly specialized, independent AI security tools (provided by third-party cybersecurity firms) to constantly audit, stress-test, and validate the structural integrity of the application.
3. The Transformation of SaaS Business Models
In the traditional software landscape, companies used the "new version release" or high engineering/maintenance overhead as a justification to lock users into rigid, expensive flat-rate subscription renewals.
Because long-term maintenance costs will drop to nearly zero for the enterprise, this justification collapses. While baseline infrastructure costs—such as cloud hosting, database storage, and compute—still exist, these have become heavily commoditized utilities. Since customers can easily run AI-managed software instances themselves, SaaS companies can no longer charge a premium for the software itself. Consequently, the SaaS pricing model will radically shift: pricing will pivot entirely to Pay-per-use (Actual Utility Consumption) or Value-Based Pricing (paying strictly for concrete business outcomes delivered, not for basic software upkeep).
4. Legacy Revamp: The Living Digital Museums
Thousands of brilliant applications, iconic video games, and highly efficient internal enterprise management tools died over the last few decades simply because companies couldn't justify the cost of maintaining them.
We are about to witness a massive wave of software resurrection. Enterprises will tap into AI to revitalize historical applications, upgrading their outdated codebases to modern standards instantly. This creates a highly profitable new market centered around recycling proven, time-tested software concepts.
5. Developers as "Context Managers"
When software becomes permanent, the most critical skill for a human engineer shifts from knowing how to write functional code to mastering the deep historical and structural context of the product.
The developer's role transforms into a Context Manager. Human engineers will guide AI ecosystems based on their profound understanding of company culture, legacy user behavior, and the philosophical vision of the product. Their job will be to ensure that endless autonomous AI updates never strip the software of the core identity that its customers fell in love with.
💡 The Takeaway: We are moving away from reinventing the wheel every few years. In the era of Eternal Software, companies will finally stop wasting creative energy on keeping the lights on, allowing 100% of their intellectual power to be spent on delivering deep, compounding value on top of an engine that runs flawlessly forever.
Topic 2: Fragmented Apps & The Rise of Identity-Driven Market Architecture
When code becomes cheap, personalization transitions from a basic product feature into a radical realignment of Market Architecture. Instead of stuffing hundreds of personalized features, tabs, and toggles into a single, bloated app to please everyone, the future of software belongs to absolute fragmentation.
This paradigm shift goes far deeper than mere tech—it taps into a fundamental human philosophy: the psychological need for exclusivity, status, and belonging to "Digital Tribes," combined with the collapsing cost of software production.
The Economic Shift: From Financial Suicide to Zero Overhead
In the pre-AI era, building, maintaining, and marketing a completely separate application for every specific niche market was financial suicide. The engineering overhead, duplicate databases, and fragmented maintenance pipelines made it impossible. Monolithic apps like Facebook or Uber were born out of this technical necessity—they had to group everyone together to survive economically.
AI completely erases this barrier.
[ Traditional Bloated App ] -> Tries to force all niches into one interface (High Friction)
VS.
[ AI Core Engine ] ----------> Instantly generates 50+ standalone "Tribal Apps" (Zero Overhead)
What will happen: A company will build its intelligent "Core Engine" (the backend, core logic, and database) just once. From there, AI will instantly generate and deploy dozens of completely standalone, white-labeled applications out of that single nucleus.
The Food Delivery Example: Instead of one mass-market food app with filters, you will download a standalone app exclusively designed for vegans, another tailored entirely around local street food, and a highly exclusive third app dedicated to fine dining. Each app features its own distinct branding, custom UI, unique tone of voice, and micro-community.
The Backend Reality: Behind the scenes, they are all powered by the exact same database and fulfillment engine. On the surface, the user feels a supreme sense of status and identity.
Overcoming the Gatekeeper and Acquisition Barriers
A common critique of this fragmented vision is the friction from app store gatekeepers (like Apple and Google) and the high cost of customer acquisition (CAC). How can a startup deploy 50 separate apps without violating app store "spam" guidelines or burning millions on 50 different marketing campaigns?
The answer lies in two major shifts:
- Adaptive Platform Policies & User Identity: App stores will edit their developer policies to support, rather than restrict, this level of fragmentation. Because platforms will know the precise identity, preferences, and real-time context of the user, they will shift from viewing white-labeled apps as "spam" to recognizing them as essential, tailored access points. The store itself will dynamically manage and authorize these custom wrappers for the user.
- Zero-CAC Identity Matching: Traditional marketing relies on expensive, broad-funnel campaigns to find customers. In this new architecture, AI discovery engines match the user’s digital identity profile directly to the specific tribal app. Instead of pushing a brand to the masses, the system dynamically pulls the user to the correct app when their lifestyle context demands it, reducing CAC to near-zero.
Look at Meta: The Blueprint of Visual & Functional Fragmentation
We don't have to wait for the future to see early signs of this psychology. Look at Meta’s ecosystem: WhatsApp, Instagram, Facebook, Messenger, and Threads. If you look closely, they all essentially do the same things. You can send messages, share images, and post stories across almost all of them. Yet, users treat them as completely different worlds because of how they anchor human identity:
- Instagram is strictly for the visual identity and aesthetic status.
- WhatsApp is for intimate messaging tied directly to your physical identity (your phone number).
- Messenger is for casual messaging tied to your social/professional graph (your email/Facebook profile).
- Threads is for text-based public commentary.
Meta had to spend billions of dollars and employ tens of thousands of engineers to maintain these separate apps and graphs over the last decade. In the post-coding era, any startup can replicate this multi-app strategy overnight for pennies.
The Core Human Metric: The Need to Feel Unique
The next generation of consumers doesn't want to feel like a row in a database of a billion users. They want to feel like the software they use was meticulously handcrafted specifically for them and their tribe.
When software is cheap, companies will cater to this craving for exclusivity. When companies split products into highly targeted, independent apps, users gain a profound sense of premium placement and community alignment. The software becomes a badge of honor, an extension of your lifestyle, and a symbol of your digital status.
💡 The Takeaway: The winner of the post-coding era won't be the company that builds a "Super App" that does everything for everyone. The winner will be the company that builds a powerful hidden engine, allowing AI to shatter their user-facing layer into a thousand brilliant pieces—giving every single digital tribe a home that feels entirely their own.
Conclusion
There are a lot of thoughts around this article, but that is enough for now. The landscape of software is shifting rapidly beneath our feet, and these shifts challenge how we define both the products we build and our roles as developers.
What are your thoughts on Eternal Software and the future of Fragmented Apps? Do you think monolithic apps are truly on their way out, or will centralized platforms find a way to adapt?
We invite you to join the discussion and share your comments below! Let's talk about where you think this is all heading.