Past the platform — AI’s next chapter
Generative AI is transforming our work, and a surge of interest and investment in the technology is occurring as a result. Some hypothesize that the large AI companies themselves will own the value that AI creates because of the moats and end user appeal they have been able to manifest so far.
I’m here to make a different case. I believe strongly that history demonstrates a consistent migration of value away from early platforms and towards the end user — in short, applications always prevail.
AT&T
Let’s take a trip back to the early 20th century. The deployment of telephony infrastructure required massive investments. AT&T (then Bell) spent nearly $2.4 billion (equivalent to roughly $50 billion today) on building out physical infrastructure.
Despite these enormous investments, the real financial gains were realized by businesses that used the telephone network to create new services and efficiencies. Industries such as finance, retail, and logistics saw their productivity soar. ROI on the infrastructure itself paled in comparison.
Of course, the infrastructure itself might have done better if AT&T weren’t mandated by the government to break up. This is a bit of a hypothetical, though; The breakup occurred precisely because the government knew AT&T had become too powerful and that more economic value existed on the other side of the breakup.
CompuServe, AOL
When CompuServe and AOL came into the picture in the 90s, dial-up internet was popularized because of how affordable and easy it became to access. Many viewed the internet as these companies and the companies as the internet. However, the real value explosion occurred not within these access services, but as a result of them. The value was on the web.
The web
The web itself was infrastructure — a platform for content and services. Attention shifted to browsers, like Netscape (later Firefox). It became “obvious” to most that if the web was where value was accumulating, then the programs that allowed computers to access it would reign supreme.
While it’s true that browsers did - and still do - provide a lot of power to the companies who maintain them, it is ultimately not where most power or value accumulated. That would be…
The use cases: eCommerce, Software as a Service, etc
Last year, global e-commerce sales reached $5.8 trillion. Trillion.
Meanwhile, the global SaaS market reached $261 billion - a measly number in comparison… until you think about the fact that all of the major telecom providers combined earned roughly the same amount, and with far less operating margin.
From Amazon to Salesforce, so much of the significant value - and profit - exists in the application of technology, not the technology itself.
But Anthony, it’s different this time.
Look, I get it. Training high quality generative AI requires a massive amount of compute infrastructure, yes. But it also requires a massive amount of training data. Neither of these things seem likely to be democratized any time soon.
There’s also an ongoing debate over whether or not a conversational interface is the application. In other words, if a company like OpenAI is both the infrastructure (the GPT-X AI model) and the application (ChatGPT), is there even room for applications ‘on top’?
While the above arguments are rational, I believe vehemently that history will repeat itself. After all, nearly every groundbreaking platform started off as both a piece of infrastructure and a “hello world” application to get people hooked:
Bell sold both the telephone service and the telephones themselves
CompuServe and AOL sold both internet access and an application for consumption and conversation
Netscape and Internet Explorer provided both web access and much of the ‘starting page’ content and tooling at the time
Despite what seemed at the time as inevitable monopolies over both infrastructure and applications, nothing of the sort occurred in the long-run. Instead, all of these companies and products were largely commoditized in favor of more competitive and compelling applications built on top of them.
It’s exactly the same.
Let’s review…
✅ General, generative AI is smart.
✅ Conversational applications are appealing in certain cases.
✅ Data moats are real.
And yet…
✅ Domain-specific and/or private knowledge layered into that is many times more valuable.
✅ New data is more valuable than old data, and requires ever-evolving mouse traps to capture.
✅ Most “human-computer interactions” are really not best had in the form of an open-ended conversation.
And so, ultimately, hype dies down. Moats shift. Sometimes, regulators step in. And most importantly, the real use cases worth paying for prevail, because markets always come out in favor of proven revenue and operating margin, not hypotheticals.
Generative AI ‘backbone’ providers - the ones training super intelligent foundation models at scale today - will profit immensely for a long time to come, no doubt. But make no mistake. These providers are a new piece of our supply chain - not where the value in it terminates.