One of the interesting opportunities I’ve enjoyed in my career is getting a ringside seat to innovative technologies that have changed the world. Being at companies that have a huge impact on their industry, including Microsoft, Amazon, and now Coinbase, means I get to experience and work on disruptive technologies every day. One particular aspect of that is experiencing the up close and personal multiple times. At Amazon I got to see the entire lifecycle of a service — from writing the initial business plan for to launching it at re:Invent to ultimately building it into one of the largest enterprise-grade cloud businesses on the planet. As VP of Engineering at Coinbase, I’m struck by some of the parallels between computing and crypto as they go through their respective lifecycles.
How is innovation perceived over time? How do you gauge the success of ideas that have large potential for change before they’re fully adopted?
Let’s look back at Lambda and Serverless computing as a way to better understand where Crypto has been, where it’s headed, and how (and when) it’s being adopted.
Every significant new technology almost inevitably suffers from being overhyped; in early days, nascent implementations, immature tooling, and emerging processes haven’t yet caught up to customer and developer needs. Let’s look back at in November 2014. At that time it only allowed a single minute of execution time, 1 GB of memory, operated in a single region, had no ability to connect to customer Virtual Private Clouds (VPCs), and didn’t even allow for synchronous (direct) invocations. It had two event sources, Amazon S3 and Amazon Kinesis, and a (very) rough “double beta” of Amazon DynamoDB change stream events.
Meanwhile, the hype was incredible — the press talked about how this new computing model could replace existing computation and applications. Customers’ excitement was unbounded as they imagined a world without servers, with the promise of infinite cloud capacity, and with perfect utilization. The initial launch presentation was standing room only, as was the repeat session added the following day. It seemed that cloud nirvana had arrived. Back in Seattle, the team toasted our launch, confident in a bright future filled with customer success and a new, growing business for Amazon.
In the ensuing months, reality set in: Running time and memory limitations, the lack of access to SQL databases (virtually all of which sat behind VPCs), the limited number of event sources, and other limitations became more apparent. Articles, posts, and tweets stopped talking about hope and excitement and began lamenting the then-current problems and limitations. Initially limited capacity in new regions and throttles on calls due to limits angered some customers. “Cold starts” and other friction from those early days caused some early adopters to declare the entire approach a failure. By the time the term “serverless” entered the popular lexicon, it was already being described as DOA by some of the very people who had initially raved about it. On the business side, adoption was growing, but volumes were modest in the first few months, and on the team we began to doubt ourselves — had we really done something unique and exciting, or was it an expensive flop?
Meanwhile, the Lambda team was working hard to remove limitations and deliver features: synchronous invocations, dozens of new event sources, larger memory, longer running times, new regions…the service quickly grew into the enterprise-grade offering that customers needed to really get value from it. And they did: In a little more than a year after launch, enterprise users became a significant customer segment. Customers who had dismissed the idea came back to reevaluate. Revenue, volume, and adoption were all growing rapidly, just like we’d been waiting for.
It wasn’t just the technology itself that needed to grow up — meeting customer expectations as a full-fledged solution required an ecosystem. For Lambda, that looked like AWS Solutions Architects who could train customers on best practices, new conferences being established, and lots of examples to help explain this new way of writing applications.
Growing into its shoes took time, in part because new technologies, like , which enables multiple hypervisors on a single EC2 instance, had to be developed in order to break through some of the limitations. This “second wave” of R&D was actually more involved than what we originally launched, in part because along the way Lambda had grown so much, so every change had to keep the “production promise” for hundreds of thousands of customers.
Meanwhile, the ecosystem was growing up, too. Application modeling and deployment tools were created, Lambda achieved compliance with major assurance programs like SOC and PCI, and formal SLAs were introduced. Third party tools and services emerged for everything from security to monitoring to support. Today, Lambda performs trillions of executions per month and has hundreds of thousands of active customers. It’s found a place in the technology portfolios of many Fortune 500 companies, with , and is considered an essential part of best practice cloud architectures. Reality caught up with that exciting initial hype — it just took time!
If you’ve been watching the crypto space, this all sounds rather familiar. Lots of initial hype (‘fiat money will be replaced!’) followed by painful reality. Falling prices in 2018 for popular assets like Bitcoin were roundly equated with crypto’s failure as a concept; a moment commonly referred to as “crypto winter” sent a palpable chill through the broader ecosystem. Meanwhile, legitimate limitations of the early technical expressions of crypto and blockchains were (and still are) mistaken for their eventual outcomes. Comparisons of Bitcoin’s 4.6 transactions per second versus Visa’s average of 1,700 called into question the idea of using blockchain as a payment processing platform. Scalability, latency, and throughput limitations, proof-of-work’s high economic costs, and the challenges some companies experienced as they grappled unsuccessfully with the realities of handling customer funds all diminished the initial luster of the approach.
But as with Serverless, the underlying technologies (along with their ecosystems of tools and processes) are growing up. Just as the hype cycle might be ready to declare crypto dead, fundamental innovation is broader than ever, and growing rapidly on every front:
Scalability and throughput are being addressed through classic optimizations, like , as well as new, crypto-native techniques including , , and . and related governance mechanisms provide a complementary model to proof-of-work, offering lower latency and aggregate power consumption.
Smart contracts and built-in application platforms are raising the level of abstraction to create a “distributed cloud” that reimagines application architecture and economics.
At the same time technology and performance fundamentals are being optimized, innovative founders are exploring expansions of the business model. Including how , , and other assets and activities can be expressed in a crypto-centric way.
And true to crypto’s origins, conventional finance is being reimagined and reinvented. Both the technology, and the businesses bringing it to market, are growing into their (very big) shoes at last.
Crypto’s “Hype Cycle”
The Gartner Hype Cycle model, applied to crypto
What determines the length of time it takes to move through the hype curve? The larger the impact of a change, the broader its market reach, and the more disruptive it is versus what came before, the longer it takes to reach the final stages, where the technology reaches its full maturity in both implementation and adoption. With Lambda, that took about three years from the initial launch. Lambda is now firmly in its “productivity plateau,” having been adopted by , with serverless now representing the technology.
Crypto as a technology, meanwhile, has the potential to change how everyone on the planet experiences financial services — its reach is broader than software development and IT efficiency, and its economic impact runs deeper than the cloud. As a result, there’s still more time to go before we reach the stage where crypto is as ubiquitous as fiat currencies and as easy to program as Serverless. It hasn’t reached its productivity plateau yet, but we’re close enough now to see what some of those improvements could look like across the entire cryptoeconomy. (Note, these are innovations we look to support both via Coinbase and our investment arm, ):
Scaling to aggregate global payment transactions per second so that every individual and commercial transaction can be captured in a blockchain.
Per-transaction latency (through L2 solutions and other mechanisms) in low-digit milliseconds, regardless of geography.
Compute and storage costs that reflect the underlying costs of cloud infrastructure and which are independent of the distribution scale of a blockchain’s network.
Tools, business processes, user interfaces, and vibrant third party ecosystems for both developers and finance workers that rival those of cloud and mobile solutions today.
Blockchains and cryptocurrencies today are where the public cloud was 10 years ago, but they’re on an amazingly fast ramp, with core R&D, customer-centric engineering, and ecosystem enablement all happening simultaneously. The innovative implementation work going on in the many open source communities and at companies like Coinbase to create new capabilities, remove restrictions, and bring compelling technologies to market is one of the most amazing things I’ve seen in my career. Hype curve, meet reality!
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