A legendary VC firm published a deep dive into the generative AI market that’s required reading.
It’s called Who Owns the Generative AI Platform?, and it comes from Andreessen Horowitz.
It shares insights from meetings with dozens of founders and operators in the space.
With the post, Andreessen aims to answer a simple, but difficult, question:
Where will value in this market accrue?
To answer it, the firm breaks down the generative AI tech stack into three main categories:
Andreessen’s conclusion is as follows:
“We’ve observed that infrastructure vendors are likely the biggest winners in this market so far, capturing the majority of dollars flowing through the stack. Application companies are growing topline revenues very quickly but often struggle with retention, product differentiation, and gross margins. And most model providers, though responsible for the very existence of this market, haven’t yet achieved large commercial scale.
In other words, the companies creating the most value — i.e. training generative AI models and applying them in new apps — haven’t captured most of it.”
Importantly, the firm also notes:
“There don’t appear, today, to be any systemic moats in generative AI. As a first-order approximation, applications lack strong product differentiation because they use similar models; models face unclear long-term differentiation because they are trained on similar datasets with similar architectures; cloud providers lack deep technical differentiation because they run the same GPUs; and even the hardware companies manufacture their chips at the same fabs.”
However, the market is well worth understanding precisely because it’s so large:
"The potential size of this market is hard to grasp — somewhere between all software and all human endeavors — so we expect many, many players and healthy competition at all levels of the stack."
In Episode 37 of the Marketing AI Show, Marketing AI Institute founder/CEO Paul Roetzer walked me through what marketers and business leaders should take away from this analysis.
The main consideration for marketers and business leaders is at the application level that Andreessen talks about, says Roetzer.
These are the actual generative AI products you use to do things more efficiently, be more creative, and improve decision-making.
As Andreessen notes, the space moves so fast, and the products are relatively undifferentiated. How are you supposed to bet on a handful of technologies to build your tech stack around?
Even the top VCs in the world don’t yet have a complete answer.
Given the confusion in the market, the best bet may be for marketers and business leaders to gravitate toward the platforms they already know, use, and trust. (For instance, HubSpot just released its own generative AI tools.)
The market is getting much more crowded. And existing platforms like HubSpot are now building generative AI into their products. That’s shaking up the ecosystem of point solutions.
“I think vendors have been losing sleep over this since ChatGPT came out, honestly,” says Roetzer.
As Andreessen notes, vendors:
“...applications lack strong product differentiation because they use similar models; models face unclear long-term differentiation because they are trained on similar datasets with similar architectures; cloud providers lack deep technical differentiation because they run the same GPUs…”
As a marketer, you could be forgiven for thinking generative AI is just about writing or creating images.
But it’s so much bigger than that, which is why Andreessen thinks the addressable market is so large.
“As crazy as it sounds to say the potential size of this market is somewhere between all software and human endeavors, it’s not actually crazy when you understand how this technology works and what it’s going to be capable of,” says Roetzer.
That’s because the large language models that power generative AI tools can do so much more than just write language. They can understand commands, which in turn can translate to capabilities in even more sophisticated knowledge work (like coding).
That has big implications for anyone who runs or invests in software companies.
“There won’t be software companies that aren’t AI-powered in three to five years,” says Roetzer.
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