Mitchell Hashimoto

Growth of AI Through a Cloud Lens

April 4, 2023

Every 10 or so years, a platform shift begins. 17 years ago, Amazon kicked off a platform shift with the release of S3 and EC2 under the Amazon Web Services (AWS) umbrella. This resulted in a shift best summarized as "cloud-native," fundamentally changing the way we build and deliver software worldwide from small individuals to the largest global companies.

Today, AI -- in particular the advancements in large language models (LLMs) -- is starting to feel like the beginning of another platform shift. This isn't a shift from cloud, this is a platform shift within a different category, but it has the same potential to fundamentally change the way we build and deliver software.

I will look at the rise of AI through the historical lens of the rise of cloud. Rather than recount a full history of cloud (boring!), I will highlight a few categories of thought and use those categories to make comparisons from cloud to AI and predictions about the future.

Immediate Value

The early success of cloud was marked by providing immediate value to early adopters. For small projects (initially, more on this later), EC2 was the fastest and cheapest way to get a server and S3 was the easiest and most reliable way to store and serve static assets and binary blobs. And everything had an easy to use HTTP API, encouraging a new age of automation and approachability for engineers.

Recent AI developments have the same immediate value feeling. Previously hard problems to quickly integrate like sentiment analysis ("is this bio offensive?") are now trivial. Code generation through tools like Copilot are not only not terrible, they're... kind of decent? And oh yeah, everything is starting to have an easy to use HTTP API, encouraging a new age of automation and approachability for engineers.

Immediate value is a really good sign and builds an early tribe of enthuastic users that make a lot of noise. At the same time, immediate value is often mistaken for durable value. The initial value of AI is undeniable, what remains to be seen is how big of an impact this value makes.

"Crypto" (as in cryptocurrencies, not cryptography) failed this test for me. It had no immediate practical value. Enthusiasts claimed it had all sorts of long-term future value. Maybe, maybe not. 🤷 All I know is someone sent me a bitcoin in 2010 and I thought "okay, now what?" And I personally never really got past that stage even to now, 13 years later, regardless of any long-term potential.

Impractical Beginnings

Early cloud computing was raw and impractical for a large tract of problems. Long-lived IP addresses didn't become available until 2008, two years after the launch of EC2. Persistent storage so you can reliably save data also didn't become available until two years after the launch of EC2. Every EC2 instance was on a shared public network until the launch of VPC in 2009, three years later. The examples continue.

The impracticality of something can't be judged in isolation. A thing is only impractical in the context of something else. So while the lack of private networks was impractical in the context of business software, it didn't make a difference for small projects and early stage startups. This led to excitement and growth in the context where cloud computing was practical. As AWS continued to launch new services, the set of contexts in which it was impractical continued to shrink.

As cloud gained some popularity and hype, a common dismissal during this era of cloud was "real businesses won't ever be able to use it." And as cloud continued to become more capable, the goal post just moved, such as "fortune 500s will never use cloud" then "regulated companies will never use cloud" then "governments will never able to use it" and so on. Today, for better or worse, important aspects of the US national defense rely on cloud.

AI is in a similar position today. Despite its immediate value, the lack of additional functionality and tooling make it impractical in many contexts. Getting the right answer reliably for certain questions is very hard or maybe impossible. Integrating LLMs with outside, up-to-date knowledge is in its infancy. A lot of tooling lacks corporate support or is currently one of a thousand seed-stage startups with no clear winner. It's early.

And just like cloud, I hear similar dismissals of the technology: "it'll never generate large amounts of code" or "it'll always require a human in the loop" and so on. Maybe, maybe not. The dismissal itself can't be immediately dismissed, but it also isn't enough on its own to ignore a trend.

Given the previously stated immediate value that AI has today, I believe this issue will sort itself out in the same way it did for cloud. The impractical parts of AI aren't that it isn't useful, its that its hard to integrate or trust the usefulness of it at scale. This is a tractable problem, not some fundamental impossibility.

There are some questions of fundamental impossibility when considering future (undemonstrated) value. As one extreme example, the excitement around artificial general intelligence (AGI) is very unproven and models that exist today are impractical for AGI. This type of impractical problem however is based on completely undemonstrated value and a different sort of definition of "impractical" than I am describing.

Evolution in Software Properties

The hallmark trait of a platform shift is forcing an evolution in software properties. Software that has these properties is usually coined with some term, such as "cloud-native" or "mobile-first." Back in 2016, I gave a talk where I outlined the properties of "cloud-native" as a shift from a static to dynamic way of thinking:

Properties of Cloud

(This is not the same slide I used in 2016. This is a more modern version that we integrated into various HashiCorp decks in later years.)

My argument was that software in the left column is "traditional" software. Traditional software can run in cloud environments, but it is inferior to software with equivalent functionality that embraces the dynamic, cloud-native approach. For software vendors in the left column, their product was also more likely to be eaten by new startups that build the same software but with properties in the right column. As an example, I often argue that Vault would not exist if the incumbents had just adapted to a cloud-native world.

An older example of the impact of shifting software properties are mobile apps. I had the opportunity in the 2010s to meet with the CEO of a large US bank. He rhetorically asked me: "what do you think is the #1 reason people switch banks?" After some wrong guesses, he told me: "more functionality in the mobile app." He was describing why the bank was investing so much money into cloud services and software engineering, and it was all going towards mobile functionality. Banks that took longer to adapt to the rise of mobile devices lost more customers.

An even older example is web apps. I think this one is obvious: you're more likely to use a service if they're present on the web. And you're more likely to use a service that has the better presence on the web.

I predict that a similar situation will arise with AI. Some sort of properties will present themselves that differentiate between "old" and "new", and the products and businesses that embrace the "new" will become more attractive to a shifting generation of users. "Old" software will not be immediately rendered obsolete, it'll just be a less attractive option compared to the "new" and as time goes on that gap will only widen.

It's too early to say what the properties will be, it takes years of maturity for these to shake out in a confident way. Given the commoditization of natural language interfaces that LLMs represent, one prediction is that all software will need some form of natural language interface at a minimum. For example, a calendar application with event generation, a command-line tool with language-guided configuration, a SaaS with an actually helpful assistant, etc. These are now all relatively easy problems to solve that provide good value to users, so it may just become a base expectation for software.

So long as incumbents in different industry categories recognize, react, and adapt to this shift, most will be fine. The core functionality of most applications is still an important necessary base prior to augmenting with AI. However, this shift will also represent a huge opportunity for new upstart ventures to capitalize on slow-moving incumbents.

Bringing Along Old Friends

I believe a really important property of early cloud computing that helped enable its success is that it had a migration story. You could "lift and shift" your way to victory -- to a limited, but sizable extent. Other big platform shifts like containerization had a similar property.

Future evolutions on top of cloud, such as Heroku or more generally platform as a service (PaaS), didn't have this property. Early PaaS was still very popular, but it didn't represent the same platform shift impact as it was often difficult or impossible to integrate with "legacy" applications.

To put it another way, new technologies that require throwing away old technologies are harder to scale to industry-wide impact than new technologies that somehow bring along old technologies.

Recent AI developments make existing software better. Yes, they enable some revolutionary new takes for certain categories of software too, but they continue to make "legacy" software better.

Ignoring various other complaints I have about it, this is another major reason I was never impressed with the web3 ecosystem. You were either a dApp or you were not. There wasn't really an in-between. I know in theory some features could be built "on chain" while others were not, but this is not how that ecosystem marketed itself as a whole.

In Conclusion

AI may be reaching its "platform shift" moment. It shares a lot of the same positive qualities and challenges I recognized in early cloud.

If this is true, we're in the extremely early innings of a very long game. If you take AWS launching S3 and EC2 as "time 0" of the cloud platform shift, then it took another decade or so for the ecosystem to mature and for long-time "legacy" incumbents to begin to be significantly disrupted.

Admittedly, I feel the hype around AI has a significantly broader social reach than cloud, so I think the time horizons on market maturity (if one develops) are shorter. But still, I predict at least many years of "open window" early-mover opportunity.

At the very least, I would caution against fully ignoring this one.