What the commoditization of search engine technology with GPT-3 means for Google and SEO

The tech-world recently geeked out after its first glimpse into OpenAI’s GPT-3 technology. Despite some kinks, the text predictor is already really good. From generating code to writing GoogleAds copy, to UI/UX design, the applications of GPT-3 have sparked the imaginations of web developers everywhere. 

This changes everything. ?

With GPT-3, I built a Figma plugin to design for you.

I call it “Designer” pic.twitter.com/OzW1sKNLEC

— Jordan Singer (@jsngr) July 18, 2020

But they should also be sparking the imaginations of search engine optimizers. In its debut week, we already witnessed a developer build a search engine on top of GPT-3. It can hardly rival Google’s product, but the potential is clearly there. OpenAI plans to turn GPT-3 into a commercial product next year, meaning any brand could use the technology to create their own search platform. 

The implications would be significant for the SEO landscape. More brands innovating their own search engines would create new opportunities for digital marketers and the brands we help build. For Google, though, the potential is far more nerve-wracking. GPT-3 has shown that Google’s core technological advantages – natural language processing (NLP) and massive computing – are no longer unique and are essentially being commoditized.

GPT-3 challenges Google’s technological edge from all directions

Few have monetized NLP and machine learning as well as Google, but their technological edge has always been computational power. Google’s ability to crawl billions of pages a day, its massive data centers, and its extensive computing across that data, have cemented their status as the dominant search engine and digital advertising market leader.

But AWS, Microsoft Azure, and other cloud services now give us access to essentially unlimited computing power on demand. A decade of Moore’s Law has also reduced the cost of this computing power by one-to-three orders of magnitude. 

Additionally, open-source software and advances in research have made it easier for developers to access the latest breakthroughs in NLP and machine learning technology. Python, Natural Language Toolkit (NLTK), Pytorch, and Tensorflow are just a few that have granted developers access to their programming and software innovations. 

Yes, Google still has BERT, which shares similar architecture with GPT-3. But GPT-3 is slightly larger (by about 175-billion parameters). GPT-3 also doesn’t need nearly as large of a training data set as Google’s BERT.

Not only will GPT-3 add value to those businesses and applications that are already using AI and machine learning with a newer, larger, and significantly improved NLP model, it would also equip Google’s biggest Cloud competitors with the ability to pair that technology with their own computing power. 

Other players will soon be able to build massive search engines like Google did

In order to build a search engine, you need to be able to retrieve many different types of information: Web results for one, but also maps, data, images, and videos. Google’s indexing power is what catapulted them to be the primary retriever of all web knowledge. 

In addition to building that massive information retrieval system, Google monetized on those economic searches through advertising. But the majority of searches Google doesn’t actually earn any money on. 

OpenAI built its own information retrieval system GPT-3 so that it could create superintelligence. If OpenAI wanted to, they could build a competitor to Google. But the hardest part would be bringing this to a massive audience. Bing’s market share is only 6.5% while Yahoo’s is 3.5%. 

It’s been a long time since the search engine market was a realistic place to compete. But what if a GPT-3 commercial product equipped a new competitor with an equally-matched technological edge, market share, cloud service, and devoted customer-base to enter the search market? 

Let’s say a competitor like Apple. They launched a newly redesigned Apple Maps earlier this year. They already announced they are indexing the web through Applebot. When it comes to launching the next best search engine, Apple is well-positioned.

How could Apple change the SEO landscape with its own search engine?

Most likely, an Apple search engine would use ranking factors similar to Google. The app store ecosystem would equip Apple with greater use of in-app engagement data. We could also see a greater reliance on social signals from Facebook and Twitter.

All android devices currently ship with Chrome + Google Search as the default search OS. Apple’s devices ship with Safari and you can select your own preferred search OS. It could easily do what Google has done and default to its own search engine. With just one iPhone model launch, Apple could transition its massive customer-base away from Google through its technological edge and dominance with devices. 

But what would be most troublesome for Google is how Apple could disrupt Google’s massive ads ecosystem. 71% of Google’s revenue comes from advertising. With Google Ads now being the most expensive (and competitive) place to advertise on the internet, many advertisers would welcome a disruption. It’s possible we could see billions of dollars of advertising revenue shift to Apple.

For SEOs and digital marketers, it’s fun to imagine. We could see entirely new markets for search, creating more need for our expertise and additional platforms our customers can use to grow. We’re not quite there yet, but SEOs and digital marketers should be prepared for what advancements like GPT-3 could potentially mean for our industry.

Manick Bhan is the founder and CTO of LinkGraph, an award-winning digital marketing and SEO agency that provides SEO, paid media, and content marketing services. He is also the founder and CEO of SearchAtlas, a software suite of free SEO tools. He is the former CEO of the ticket reselling app Rukkus.