Don’t Future-Proof Features. Future-Proof Value.
I spent three months working with a team creating a blog generator. The problem was simple: use AI to help small business owners who weren’t good at writing quickly create stories around their products and processes. But shortly into development, we hit the context window limitation – where large language models (LLMs) can only process a certain amount of text at once.
My solution? Break everything into chunks. It was elegant. Split the content into perfect bite-sized pieces, process each chunk individually, then stitch it back together. We added clever handling to maintain context between chunks. The team was proud. The code was clean. The architecture was strong.
Then Claude doubled its context window to 200,000 tokens.
Three months of careful engineering made obsolete in a single product update. But you know what? I should have seen it coming. We all should. Because this isn’t a story about context windows or chunking solutions. It’s about a fundamental shift in our product-building approach.
And the thing is, this happens all the time. In the span of a single week in March 2025, OpenAI’s GPT-4o, Ideogram 3.0, and Reve all solved the text-in-image generation problem. Suddenly, companies that had built entire products around complex text overlay solutions faced an uncomfortable reality: they had no unique value prop. Their moat had dried up completely.
This is the new normal in AI development. The question isn’t if today’s limitations will be solved, but when. And if you’re building products based on current AI capabilities, you’re already behind.
How do we build for a future that changes this fast?
The Pace of AI Innovation
Most companies operate on timelines measured in quarters, sometimes years, and that’s OK. The challenge isn’t matching the pace of AI, you won’t. It’s about looking far enough ahead to see where those capabilities are heading.
When AI evolves rapidly, the critical skill isn’t faster development – it’s better foresight. What’s cutting-edge today might be table stakes tomorrow, and the teams that thrive aren’t necessarily the ones that code faster; they’re the ones that anticipate change and build to it.
Let’s dive in further on the text-in-image issue. For years, it was a seemingly unsolvable problem that spawned dozens of startups and workarounds. Companies built entire product lines around overlay systems, template engines, and custom OCR tools. Investors poured millions into these ventures. Development teams spent countless hours optimizing solutions.
Then, in a single week, three different companies solved it. Not just solved it but solved it so completely that the previous solutions looked like using a typewriter to send an email.
This wasn’t a fluke. It’s a recurring pattern. Each wave of AI advancement renders entire categories of solutions obsolete. Image generation made photo editing apps redundant. Multimodal capabilities, the ability for an LLM to understand not just text but also images, audio, and video, eliminated single-purpose tools. Expanded context windows made complex document processing look quaint.
None of these advances were surprising. If you were paying attention, you could see them coming. The writing wasn’t just on the wall – it was generated by AI, perfectly rendered, in your preferred font.
The real question isn’t whether AI will solve your product’s core technical challenges. They will. The question is whether you’re building something that will still be relevant when they are.
Shifting from Limitations to Possibilities
In software development, there’s a natural tendency to build around current technical constraints. But when those constraints are falling every few weeks, this approach becomes a liability.
Think about the early days of mobile apps. Some people built their entire architecture around handling slow cellular connections and limited bandwidth. I know because I was some people. Our team spent months building a hotel app optimized for low connectivity scenarios. We created elaborate systems to ensure guests could still use their phones for hotel activities even without an internet connection. The offline-first architecture was our key differentiator, the first words out of our sales team’s mouths.
But between rapidly improving cellular networks, increasingly reliable hotel Wi-Fi, and the overall acceleration of connectivity infrastructure, our killer feature was a killer feature for about six months. What we thought was being adaptable by design turned out to be technical debt that slowed us down as the world sped up.
We’re seeing the same pattern with AI, but at hyper-speed. Remember those image generation companies that spent months perfecting their finger-rendering algorithms? They were solving yesterday’s problem while their competitors built for tomorrow’s capabilities.
What’s the alternative?
Instead of asking “How do we work around this limitation?” smart teams are asking “What would we build if this limitation didn’t exist?” Because soon, it won’t.
This isn’t just philosophical – it’s practical. When building that blog generator, I should have created an architecture for any context window size. The chunking solution shouldn’t have been baked into the core product. It should have been a temporary adapter, easily discarded when no longer needed.
The key is spotting which limitations are temporary. Here are some questions that can help:
- Is this a fundamental constraint or a current technical limitation?
- Are multiple companies or research teams working to solve it?
- Would solving this limitation unlock significant value?
If you answer yes to two of three, you’re probably looking at a hurdle, not a wall. It’s not whether AI will solve your product’s core technical challenges. It will. It’s whether you’re building something that will still matter when those challenges are solved.
A Framework for Future-Proof Development
Building products that thrive and provide value in the AI landscape requires more than just identifying which limitations will fall. Having watched numerous solutions become instantly obsolete (and being responsible for a few myself), I’ve developed a framework based on three key horizons:
- Current Capabilities: What’s possible now.
- Imminent Breakthroughs: What’s likely to be possible in 3-6 months.
- Fundamental Challenges: What core human elements will remain complex regardless of AI advances (Building trust, navigating ethics, or truly understanding human context).
Success lies in building your core value proposition around that third horizon, while leveraging the first two as accelerators. Strike the balance: don’t depend on current limitations, but keep your solutions grounded in reality rather than science fiction.
To apply these horizons effectively, ask yourself these questions:
- What problem are we really solving?
- What would the ideal solution look like if technology had no limits?
- What’s the smallest step we can take toward that ideal solution?
For example, with our blog generator, the real issue wasn’t about chunking text – it was about helping small organizations tell their stories effectively. The ideal solution wouldn’t involve technical workarounds; it would simply understand and enhance their natural communication style.
Take those companies building text overlay solutions for AI-generated images. The ones that will survive the recent wave of breakthroughs won’t be the ones with the best overlay technology. They will be the ones focused on helping creators tell visual stories effectively. The text rendering capability was a temporary hurdle on the way to their real mission.
This approach requires a different kind of courage. It’s easier to solve today’s concrete problems than to build for tomorrow’s possibilities. But in today’s fast-paced tech world, the biggest risk isn’t being wrong about future capabilities – it’s being right too late.
Building for Tomorrow, Shipping Today
The trick isn’t just identifying which limitations will fall. It’s building flexible systems to capitalize when they do. Think of it like building a house with room to grow. You don’t need to construct every room today, but you need the foundation to support them in the future.
Make temporary limitations pluggable.
Clever solutions to current AI limitations have a remarkably short shelf life. The chunking system should have been a swappable component, not a core feature. Build your foundation around permanent user needs, then treat technical limitations like furniture – easy to replace when something better comes along.
Create clear upgrade paths.
Start with current capabilities but design interfaces for evolution. For the hotel app, we should have created an architecture where offline functionality was a feature, not a foundation. The best systems grow stronger, not obsolete with technological advances.
Stay focused on user needs.
This is your North Star. People don’t want chunked content or offline apps – they want to tell their stories and stay connected. Focus on what users want to achieve, not how technology forces them to achieve it. Let your roadmap be guided by user goals rather than technical limitations.
The companies that will thrive in this AI-accelerated landscape won’t be the ones with clever workarounds. They’ll be the ones that see where technology is heading and build products that improve, not become obsolete, when it arrives.
The New Innovation Playbook
The AI development pace isn’t slowing down; it’s accelerating. Every week brings new capabilities that turn yesterday’s impossible into today’s baseline expectations. You can’t outrun this wave of innovation, but you can surf it.
AI will inevitably solve your current technical challenges. The real challenge? Building something that will still provide value when it does.
Tomorrow’s coming faster than you think. Make it count.
If you still have questions on AI value development, the team at Doyon Technology Group can help. Connect with us today at connect@doyontechgroup.com to get the conversation started.
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About the Author
Greg Starling serves as the Head of Emerging Technology for Doyon Technology Group. He has been a thought leader for the past twenty years, focusing on technology trends, and has contributed to published articles in Forbes, Wired, Inc., Mashable, and Entrepreneur magazines. He holds multiple patents and has been twice named as Innovator of the Year by the Journal Record. Greg also runs one of the largest AI information communities worldwide.
Doyon Technology Group (DTG), a subsidiary of Doyon, Limited, was established in 2023 in Anchorage, Alaska to manage the Doyon portfolio of technology companies: Arctic Information Technology (Arctic IT®), Arctic IT Government Solutions, and designDATA. DTG companies offer a variety of technology services including managed services, cybersecurity, and professional software implementations and support for cloud business applications.