Unexpectedly, the prior wave of value from AI roughly all went to incumbents over startups, despite a lot of startup activity.

In the first internet wave most of the value went to startups (Google, Amazon, Paypal, Ebay, Salesforce, Facebook, Netflix) while some was captured by incumbents (Microsoft, Apple, IBM, Oracle, Adobe) who extended their franchises onto the internet. Perhaps this was a 60:40 or 70:30 startup:incumbent split.

For mobile, most of the value went to incumbents (Apple, Google, and then every mobile version of an incumbent’s app - e.g. “Mobile CRM” was not a stand alone startup but rather Salesforce on your iphone) while there will still significant capture by startups (Whatsapp, Uber, Doordash, Instagram, Instacart etc). Perhaps this was a 20:80 startup:incumbent split.

Crypto in contrast has been roughly 100% startup capture (Bitcoin, Ethereum, Coinbase, Binance, FTX, etc) with very little participation in value creation by existing financial services or infrastructure companies.

While there were many “AI first” companies over the last decade (prior to the current transformer and unsupervised learning revolution) the really big AI applications landed with Google, Facebook (newsfeed and ads), Tiktok (Bytedance), Netflix (recommendations), Amazon (Alexa) etc.

To beat an incumbent as a startup you usually need to either build something so dramatically better that you overcome the distribution, capital, and pre-existing product moats of the incumbent, or you need to focus on a brand new customer segment or distribution moat the incumbent can not serve for some reason. In general you need a 10X better product.

Perhaps incumbents won due to a data advantage that is now going away as companies use the broader internet as an initial training set + are switching to models that work more robustly against smaller data sets?

Many prior-wave AI companies either directly took on incumbents or worked in hard markets. Hard markets include things like education or healthcare, where technological innovation is often crushed by market structure, regulation, or a seeming indifference to actual end-user needs by people already in the field.

While many of the prior innovations in AI were striking and exciting (AlexNet, CNNs, RNNs, GANs etc) this time does feel different for a few reasons. There is reason to believe while incumbents should capture a good amount of the value in this wave, startups will take a bigger share of AI generated value this time around.

One of the remarkable things about this current technology wave is the speed of innovation across many areas.

This time, the technology seems dramatically stronger, which means it is easier to create 10X better products to overcome incumbent advantages. The "why now" may simply be a technology sea change.

GPT-3 seems to be useful but not "breakthrough" useful to the point where large numbers of startups are building big businesses on it yet. This could also just mean not enough time has passed since it launched recently. However, a 5-10X better model then GPT-3 should create a whole new startup ecosystem while also augmenting incumbent products.

Unlike the prior wave of AI startups, there are a clear set of infrastructure-centric companies with broad adoption and rapidly growing usage - this includes OpenAI, Stability.AI, Hugging Face, Weights and Biases, and others.

There are highly repetitive, highly paid tasks (code, marketing copy, images for websites etc)

Workflow tools do not exist or are weak for the use case, so the AI features become a core and useful part of a broader workflow tool

Summarization or generation of text or images is useful for the product application - this is enabled in a high fidelity way by new AI tech in a way that did not exist before.

The key with all this exciting tech will be to avoid the hammer-looking-for-a-nail problem. It will be important to identify actual end user needs and unserved product/markets that will benefit from this wave of exciting technology.

After having personally worked for 15 years on AI-related products directly, or investing in them, it feels like startups will finally start to get real value from AI. Exciting times lie ahead!