AI Disruption Déjà Vu
What The Past Tells Us About the Future of AI
I get asked a lot about my opinions on AI and its future. As technologists, I feel we spend so much time looking forward that we forget to look at what stood before. There’s plenty of hyperbole out there, but my views tend to be more grounded; shaped by what I’ve seen play out in the past.
History shows us that truly disruptive technologies tend to follow a similar trajectory. From cloud computing to mobile to AI, the pattern repeats, with each stage setting the foundation for the next.
So, here’s my view of where we’re at, where we’ve been, and what might lie ahead for us in the dizzying world Artificial Intelligence.
Breakthrough
A breakthrough occurs when a technical achievement suddenly becomes visible and accessible to the public. For AI, this was the arrival of tools like ChatGPT, DALL·E, and Midjourney. They weren’t just new apps, they were cultural events that captured imagination and gave non-experts access to something that previously lived in research labs.
Competition
Once the potential is clear, the gold rush begins. Multiple players rush in to stake their claim, each racing to build better, faster, or cheaper versions. We see rapid improvements as vendors compete for mindshare and market relevance; even if most of them are solving the same problem in slightly different ways.
Commoditisation
Eventually, the core tech stabilises and a few platforms emerge as dominant. Differentiation slows. Instead of trying to beat the top players (like OpenAI or Anthropic), others shift their energy to building on top of them. Creative momentum returns. The focus moves to solving real problems, building useful products, and designing better experiences. Trust, governance, and alignment start to matter more: which platform should you bet on?
Integration
With little room left to compete on raw capability, providers look for new ways to maintain market share. AI becomes an embedded feature of other products. It shows up everywhere … in email, customer service platforms, productivity apps, design tools, and more. It’s no longer just a product; it’s a vital ingredient.
Networking
As the ecosystem explodes, a new problem emerges: fragmentation. Users love having access to powerful tools, but hate their siloed nature. They demand interoperability. Over time, vendors respond with protocols, APIs, or standardised runtimes that allow technologies to connect, delegate, and collaborate. In the cloud world, when Google was losing ground to AWS, they released Kubernetes as a common, unifying layer. We’re seeing similar moves in AI now with initiatives like A2A and Model Context Protocol (MCP).
Specialisation
Over time, the once eye-watering costs begin to drop and efficiency improves. The technology becomes portable, cheaper, and more flexible. It becomes viable in industries or environments that previously couldn’t support it. We start to see domain-specific solutions emerge; tailored versions optimised for healthcare, finance, manufacturing, education, etc. What began as general-purpose evolves into specialised vertical stacks. Each one kicking off its own inner loop of innovation.
Recomposition
Eventually, we start seeing new, innovative uses of the technology that reimagine existing markets. Uber didn’t just make a taxi app; they combined GPS, mobile payments, and real-time coordination to create something game-changing. With AI, we’re starting to see the same: novel combinations of capabilities that disrupt existing markets, often in subtle but powerful ways.
Adjacency
The final phase is the least predictable, and arguably the most exciting. Entirely new products, industries, or behaviours emerge that weren’t previously possible. It took a combination of disparate innovations and cultural shifts for YouTube to create something new. We don’t know yet what AI’s adjacent industries will look like, but they won’t be improved versions of what we see today; they’ll be something entirely new, enabled by AI, not defined by it.
Final Thoughts
This cycle isn’t unique to AI. We’ve seen it with the internet, mobile, and cloud. Each time, the phases might play out slightly differently, but the core pattern holds. Hopefully, understanding where we are in that journey can help you cut through the noise and focus on what really matters next.