Re-founding, reInventing and the Future of AWS

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Four weeks ago at the company’s Universe event, in a move that proved controversial in some corners, GitHub CEO Thomas Dohmke announced that the company was now “re-founded on Copilot.” This was a bold statement that some apparently viewed as a potential abandonment of Git and its founding principles, which alarmed those with little to no interest in having a generative AI system assist their development. The reality was presumably less about deprecation, however, and rather intended to reflect a simple truth: one way or another moving forward AI is going to play a role in most developers’ workloads and the company’s products are going to reflect that.

Fast forward to last week. AWS’ reInvent, the name notwithstanding, made no similarly grandiose and explicit promises that the company would be rebuilt on AI. But it didn’t have to. The keynotes spoke volumes on that score.

Normally a sprawling event announcing hundreds of disparate services with no real larger narrative attempting to stitch them together other than messages like “primitives not frameworks,” reInvent this year was an AI event first and everything else second. With the notable exception of Werner Vogels’ Thursday closing session, the rest of the keynotes were dominated by AI related subjects.

This was understandable for two reasons. First, and most obviously, as GitHub’s event demonstrated only weeks prior, AI is currently reshaping our industry with a speed and breadth that is arguably without precedent this century. Virtually every event, then, is now an AI event in some way. Less obviously, however, AWS’ overwhelming focus on AI can be best understood as a response to an industry perception. For the past seventeen years, AWS has paced the industry, kicking off wave after wave of technical innovation from cloud compute and storage to managed databases to function as a service and serverless to, well, the list is long.

GitHub’s Copilot, with all due respect to startups like TabNine that have paved the way, was the first offering from a well-resourced hyperscale subsidiary that caught mainstream attention following its launch in October of 2021. But it was OpenAI’s ChatGPT, launched little less than twelve months ago that really blew the doors off. There’s a reason that that technology set records for adoption, hitting 100M users in two months, and that reason is that the technology was a revelation.

In contrast to prior technical waves RedMonk has observed such as containers, distributed version control, NoSQL or even cloud, conversational AI is relevant – or at least potentially relevant – to virtually every employee in an organization, regardless of role. Containers grew explosively while only really being technically relevant to developers and operators in the early days. ChatGPT had no such limitations; it could write marketing or sales copy, if imperfectly, as well as output code. The early versions were clunky – it answered the first question asked here incorrectly, but worse, very subtly so – and even the latest iterations still make basic mistakes. But the potential was clear then, and it’s clear now. And the systems are getting better quickly, as evidenced by the fact that they now know the correct number of how many World Championships the Red Sox have won this century.

The problem, then, is that nowhere in the above two paragraphs briefly describing the arc of innovation we find ourselves in is the abbreviation AWS. For the first time in nearly two decades, AWS found itself on the outside looking in for what may prove to be the most transformative wave of technology adoption since the internet itself. Never mind that Google – long regarded as having some of the best talent and tools in the artificial intelligence space, not least because it published the paper that made ChatGPT possible five years before it was released – found itself in the same position. AWS was used to being the 800 pound gorilla, and if that’s the role you’re accustomed to, being a bystander is unacceptable.

So AWS got to work and did what AWS does, which is spin up new services with a speed that is the envy of the industry. reInvent represented the company’s best opportunity to tell said industry about its vision for AI moving forward, and how it would impact the company and its customers.

To judge from keynote airtime, at least, that vision is centered around models. Specifically the choice of models. There’s not much debate that different models will have different strengths and weaknesses. A model designed to replication vision, for example, is likely to be better at that task than one built to generate music. Training data, model design and refinement all end up producing both models that can handle a variety of tasks and those that are more tightly specialized. In addition to capabilities, users – or at least their employers – will have other concerns about models. What are they trained on? How will my data be shared and used, and how do I know it won’t be exfiltrated? How fast is it? How much does it cost? And so on – there will be questions about models.

reInvent, however, represented a rather large bet at least from an airtime perspective that models will be the primary concern, not a secondary consideration. On paper, this seems to make sense. AWS, dating back to last year’s Bedrock announcement, has attempted to differentiate itself from the market, which is to say primarily from Microsoft and OpenAI, by offering its own model choice versus their more closed model.

It’s not clear, however, that that’s going to be the selling point that AWS seems to believe it will be. Much yet depends on how these technologies are adopted, of course, and that is an open question. The sheer growth rates of tools like ChatGPT (0-100M users in two months) and Copilot ($0-$100M run rate in two years) is evidence that these tools are popular for practitioners, as are the examples too numerous to count of teams explicitly forbidden from using them that are using them anyway.

The top of the organizations, meanwhile, are either evaluating these tools at present or being pushed to by boards that are reading breathless new AI headlines in even mainstream papers on a daily basis and asking, “what’s our AI strategy?” The question, therefore, is what happens when most or all of a developer population is already using one or more tools and an organization’s leadership wants them to use a different one.

The history of AWS itself may perhaps be instructive here. At reInvent in 2018, then CEO Andy Jassy said the following:

When we got started, we noticed that developers were being ignored – I’m not sure why, maybe it’s because they didn’t have money, and they were largely constrained in terms of what they could use.

What Jassy was suggesting was that AWS’ success was due at least in part to fleets of developers imprinting on AWS cloud offerings, making their adoption within an organization something of a fait accompli. In an era of developer empowerment, it’s difficult and rarely wise to attempt to impose technologies on unwilling developer populations top down.

If we assume that developers will have some and potentially a major role in adoption of generative AI technology, then, the logical question to ask is how much they care about models. The answer is complicated. On the one hand, hubs like Hugging Face are vibrant communities with large, active developer populations. Populations that are there to engage around models, to tinker and to seek out with precision options that fit their particular, and often very niche, use cases.

On the other hand, choice can be a burden, and if faced with a decision between a tool they’ve become accustomed to such as ChatGPT or Copilot and the choice of a half dozen or, more problematically, thousands of models, most seem to opt for the devil they know. After testing Google’s Bard vs ChatGPT recently, for example, one developer determined that, in his opinion, Bard provably wrote better Python. He did not, however, switch to Bard because of this. He remained with ChatGPT, in his words, because it had his long chat history, because he was used to it, and because he didn’t just write Python. There’s a curiosity about models, clearly, but that doesn’t necessarily correlate with practical usage, and there’s a difference between tinkering with open source models on the side and leveraging generative systems in your day to day workflow.

In some ways, AWS is following in Google’s footsteps. Just as Google chose to compete with a clear and closed market leader in Apple with openness via the Android platform, reInvent was AWS’ attempt to sway executives on the basis of its greater choice and flexibility with models versus the hermetically sealed Microsoft/OpenAI continuum. Mobile devices and generative AI infrastructure are very different markets, of course, but the dynamics of how iOS versus Android are notable nevertheless.

To be clear, AWS’ open model play will have some success, as organizational leaders have real concerns and trust issues with Microsoft and OpenAI, and Google to date is not a more open alternative. But if developers are uninterested in and unimpressed by AWS’ extended discussions of the potential benefits of its myriad of available models – particularly the more enterprise focused of these like privacy or data sovereignty, might the company risk losing their hearts and minds to those providing a vision of AI that is focused on the actual experience of using the tools, not the background models that they can’t see?

Perhaps the most interesting aspect of this outsized focus on models at reInvent is that there was a ready alternative to hand, the questionably named new product Q. Q was of course announced at the show, to much fanfare and acclaim, but got far less time on stage than the plethora of other models and options the company had on display and far less time than one might have expected. Given the timeframes involved, it seems probable that Q was built very quickly – and potentially, given some of its reported hallucinations, too quickly. But the market has come to understand that hallucinations are par for the course for new AI products, and are the very definition of a solvable problem. While they make for problematic headlines, then, they don’t suggest much about the product’s future. And the product’s future could be interesting indeed.

As has been documented both here and elsewhere repeatedly, AWS velocity has inevitably resulted a surplus of options; a sprawling catalog of services that is increasingly too much for developers to navigate without assistance. Q was built, in part, to provide that assistance. It may not be quite up to the job yet, but it offers a vision of how it could be.

In recent years the market has experienced something of a renaissance in what were once referred to as PaaS platforms. From Cosmonic to Fermyon to Fly.io to Netlify to Render to Vercel, the appetite for abstractions above base cloud primitives is growing, and growing quickly. What’s clear from the market is that there is unlikely to be a one sized fits all, general purpose PaaS platform capable of addressing an entire market. Instead, multiple specialized platforms with individual areas of focus and specialty have emerged. What’s less clear at this point is whether, and how, a conversational AI platform might emerge as another credible option in that space. Is there a future in which Q, for example, could shield users from a growing variety of development, implementation and operational tasks and serve as a de facto PaaS or something like it? Not for every workload, clearly, but then none of the would be next generation platforms can offer full coverage. That seems at least possible, and if executed would represent a potential solution to one of the company’s greatest current challenges: the size, scope and breadth of its own product catalog.

But that was not the vision that AWS chose to emphasize at its annual event. Whether that was out of a realization that Q wasn’t fully baked yet or out of a genuine belief that choice and optionality of models will be a compelling, market moving differentiator wasn’t clear. As competitors like the GitHub, Microsoft and OpenAI combination continue to push the boundaries of imbuing every aspect of the developer experience with AI, however, it will be a strategically important question for AWS to answer.

Disclosure: AWS, GitHub, Google, Microsoft, Render, and Vercel are RedMonk clients. Cosmonic, Fermyon, Fly.io, Hugging Face and Netlify are not currently RedMonk clients.

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