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It Was Always Infrastructure
Finding and addressing knowledge gaps in emerging tech
Hi friends 👋,
I’m so glad to be back with my first article in a long absence of posting. Whereas my writing last year was focused on breaking down Web3 from the perspective of an outsider, I’ve since been full-time in Web3 for nearly a year! I hope to offer more nuanced takes from my perspective now on the inside.
This article is meant as a gentle reintroduction to Future Proof. When we last left off, Web3 was ripping and roaring. It was the talk of the town! Now, it appears to have suffered a critical (but not fatal) blow. From my own perspective, what has really been stripped away is the loud, narrative-driven parts of Web3. The core infrastructure that really propels blockchain applications forward are relatively unaffected. In fact, it feels like there’s more space to talk about what actually matters. Despite this, an incriminating question still lingers on my mind: What caused our collective attention, labor, and capital to be so poorly allocated?
Today, I reflect on some of my past experiences - mainly across different technology sectors - where this dynamic has played out before. I believe it is a symptom of putting too much faith in narrative, and not enough time to understand the roadmap of technology itself.
Let’s get to it 🚀
It Was Always Infrastructure
With great confidence I can say the most esoteric chapter of my career has been working in quantum computing. I know this because in any professional interview, the resume item that reads “Developer, Quantum Software” has never failed to elicit a question. With my interviewer’s interest piqued, I field questions that range from timid curiosity to boundless enthusiasm. After illusions of sci-fi grandeur are (regrettably) dispelled, I’m usually faced with the archetypal closer:
“What was the greatest lesson you learned during your time there?”.
I give the same answer every time:
Their eyes narrow, clearly disappointed I haven’t unraveled the deepest mysteries of our universe. No, my greatest takeaway wasn’t the grueling mathematics, nor was it some occult knowledge learned at the boundaries of physics. My most important lesson was one on human nature.
The unfathomable truth
You see, our job was to build an easy-to-use software package that would offer developers the benefits of quantum computing while hiding its innate ‘quantum-ness’. I worked on a proof-of-concept for a nearby hospital. But as time pressed on, it was clear we bristled against the limitations of nascent technology. Quantum hardware simply wasn’t up to the task of working on even the smallest of datasets.
So we did what any good startup team knows to do: existentially scrawl on a whiteboard. Our co-founder drew a line from one end to the other, with markings for every year going forward: 2019…2020…2021…all the way to 2025.
“Perhaps then we’ll be able to solve some real problems for our customers”
On display was the patience of a lifetime academic; the rationality of a researcher who knew where technology truly was, not where they wished it could be. It was unfathomable to me - then an enthusiastic intern with an optimistic and overly-romantic view of technology. Thank goodness I wasn’t in charge. The decision was made by more experienced minds.
We would stop solving for the fleeting use cases of unlikely customers, and instead work on research and infrastructure that would push back the very limitations we had faced.
Chasing problems, not promises
This decision is constantly encountered in emerging tech. In fact, it might be exactly what distinguishes emerging tech from everything else; a special point in time when builders are caught between their customer’s expectations and imperfect technology. It’s what I faced in quantum. It’s what’s happening now in Web3. But for our sake, let’s first take a look at a more developed example:
Not too long ago, around the time when “Adopt AI!” became a corporate battlecry, there was a veritable gold rush to use machine learning anywhere that data could be found. PhDs were hired at great cost while Masters students were practically bribed out of their programs. Developers from all corners made lateral career moves towards the shiny new role of data scientist. Industry puts its full weight behind the promising new domain of machine learning. And yet, widespread integration of ML into products still took years to appear. Why?
Well, at the time training models was expensive and time-consuming. Preparing training data for training was iterative and manual. To deploy a model, let improve it with real-time updating, was an unknown frontier. Though industry was primed for adoption, the infrastructure needed to support that adoption wasn’t ready. This is why some of the earliest success stories in ML are MLOps companies (tooling to support developers during the ML life cycle). For ML to go from emerging to emerged, it needed a few more technological stepping stones to deliver on its promise.
Great entrepreneurs recognize this, and direct their efforts towards filling these gaps, not building on top of them. This is the pivot done right; capable teams abandoning their speculative problems to work on more assured ones. It’s a process of profound economic importance, but one that can be easily disrupted.
There is no better example here than Web3, where it’s hard to tell if the past year has seen more technical breakthroughs or speculative fervor. Just recently, companies spent millions of VC dollars in a race to capture the consumer. But this proved a race to the bottom. Today, even the best consumer-facing apps are waiting on core improvements in blockchain technology to deliver on their promises to users.
In this position, the best companies distinguish themselves by chasing problems down to their roots - also referred to as ‘verticalizing’. Instead of being blocked by technical shortcomings, these companies seize the opportunity to deepen their expertise. In today’s Web3 environment, it’s easiest to find an edge over your competitors simply by going deeper in the stack. Here, you can finally overcome your limitations, or better yet, adapt your business altogether to address a more fundamental problem felt by all.
This process of finding and filling in technical gaps is at the heart of emerging tech. Because ‘emerging’ doesn’t have to mean ‘broken’. Instead, it should mean we are always progressing. Our call-to-arms as builders is not just to dream big and take on technical risk, but to see the present moment for what it is and build what is essential.
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With gratitude, ✌️