AI’s rise, Quantum Lessons: Dispelling the myth of novelty

The world of technology is a highway with many lanes. Some zoom toward bleeding-edge research, while others navigate the realities of today's challenges. Quantum computing often resembles a high-speed lane, surrounded by cutting-edge research and experimental breakthroughs. For many companies, this represents an expensive toll gate, headed toward a distant horizon – a "Horizon 2" or "Horizon 3" event.  

This seeming roadblock to progress reminds me of AI's journey. The technology wasn't always mainstream.

Consider this: AI has been arriving to reshape the world for about 70 years, evolving from a novelty to a business necessity. The accessibility of AI exploded with the advent of transformer models, which led to the emergence of Gen AI and Large Language Models (LLMs). Access, ease of use, and engagement provided frameworks obliterating the barriers to entry. 

Here's the good news:

Learning from AI's evolution can demystify emerging tech like quantum computing.

Relating these lessons to quantum computing makes it more accessible, bridging the gap between the "fast lane" and the "middle of the road."  

So here are four concepts to keep in mind.

You may already leverage them in your toolkit, but I'd encourage you to reframe your thinking for emerging exponential tech like quantum.  

Thought one: Technology progression sets the stage for ecosystem partner opportunities.  

As technology evolves through phases – from genesis (invention) to custom (usable to those invested in commercializing it) to productized (well-known feature sets) and last, commoditized (price competition sets in), opportunities evolve. That's to be expected.   

At "genesis," you have the foundational partners -  – think Microsoft with Open AI. 

At "customization," you have those that resize and reshape for fit. Think of consulting companies working with enterprises to take AI models and build industry-specific use cases. These are partners who drive technology flexibility alongside you.   

At "product," you enter the realm of operational efficiency with the first productization. Think of AI assistants that can be adapted with little tweaks to the given industry. This is where ecosystem partners can interact with a broader market to achieve scale.   

Last, you hit "commoditization," where most entrants have demonstrated utility, so it's price that differentiates offerings.  

What's interesting is that partners in one stage can seek returns on their initial ambitions, moving onto the next stage.         

Take Microsoft , for instance. The company started at Genesis with OpenAI and are now productizing the technology with Co-Pilot.  

The point is that while each stage brings its own complexity, building partnerships with an eye toward the horizon is crucial. This means not partnering for the sake of it, but developing a strategic alignment with partners who can contribute to the progression and evolution of technology. Such partnerships can accelerate the pace of technology advancement, making it possible to reach the next stage faster than would have been possible alone.  

Thought two:  The way people are embracing quantum computing mirrors the adoption curve of AI.

Let's connect the dots, shall we?  

As with AI, quantum adopters can be grouped into interesting categories.

Participants range from the visionaries laying the groundwork to the tech titans shaping the future to the early adopters who spot the potential in tiny variations. 

Wait a second though, let's address the nuance leading to other potential partners. Those who are sitting on massive data sets eager to harness the power of quantum for previously unsolvable insights and automation bring a different value. Companies are more prone to examine their own data than find expanded value through exogenous data sets. Forward-thinkers are another category. They explore how to monetize quantum applications for their business, as well as developing services and solutions that serve the greater market.  

Here's where it gets fascinating: remember the plug-and-play crowd in AI? These folks buy into AI solutions, driving adoption at scale; they're the ones making AI mainstream. They may be early adopters following the evolution. They may be fast followers who spot opportunity but their role can be vital to crossing the chasm between limited applicability and broad use. 

Now, envision a future where quantum computing is as accessible as AI, a world where anyone can tap into quantum solutions with ease. This is the direction we're heading, a future brimming with limitless potential and exciting opportunities.  

 Let's take a moment to acknowledge that while Quantum's potential is vast and exciting, it's not a one-size-fits-all solution—at least not at this stage of the game. This means there is no plug-and-play equivalent in Quantum (at least not yet), but that is the vision of the ecosystem every technology needs to drive to— especially Quantum.          

Thought three:  So, here's the reality check: like with AI, most people won't be building their way to quantum inclusion – they'll buy their ticket on the quantum train.  

You know what? That's okay, it's advisable for most businesses and partners.  

Consider this: resources for new technologies are scarce and often come at a premium. Building a quantum team overnight is not always feasible. However, a strategic investment in quantum solutions is a smart move. The approach allows you to leverage the expertise you need without unnecessary reinvention, empowering you to make the most of this emerging technology.  

Leading us to Thought four:  The AI-first approach is perfectly valid. It's like laying the groundwork for a sustainable future, building an ecosystem where AI leads the charge and Quantum follows suit.  

Imagine this ecosystem evolving through three phases: understanding, extending, and embedding. It's like watching a seed grow into a mighty tree, each stage bringing new growth and possibilities.   

Picture AI is leading the charge in quantum exploration (Understanding), which involves learning about the principles and potential applications of quantum computing.  

Then, seamlessly transition to a phase where AI still leads but quantum complements (Extending), which involves using quantum computing to enhance AI capabilities.  

Finally, we reach a stage where AI and quantum work hand in hand, each leading in their own right (Embedding), involving a full integration of quantum computing into the AI ecosystem.  

It's a journey of maturity where you partner for the stage you're at, focusing on the right types of technological advancement and collaboration to drive progress. 

As they say, change is coming.   

Remember, quantum is coming. The timeline is uncertain but the progress is undeniable.

By taking these key lessons from AI as an instructive exemplar, you'll be well-positioned to adapt and integrate quantum computing as it becomes a mainstream solution.          

PS: This is an opinion piece and may not reflect my employer’s official views.