Foreword
If followed closely, it’s safe to say that the internet is undergoing its largest pivotal shift since the introduction of search engines around 2000s. Many things from user experience to infrastructure have been shaken up during these last two decades, from read-only websites to write-features enabling the platform economy to flourish.
Some of the most valuable companies have based their success on the web as we remember it. Not much has changed in the way we consume internet since the introduction of the iPhone and App Stores.
At STRGL, we believe that the best is still yet to come.
Era of Platform Web
- Point of entry: Remember how we used to browse the Web? Some of us knew domains directly from hearsay, yet most of us started from search engines. We got used to the URL bar being Google by default. We learned to explore and navigate the internet through the services built by some of the largest companies in the world.
- Web experience: We went back and forth between different search engines and websites, to services some of which were ad-supported and some had paywalls. Websites and services were fighting for our attention through SEO (Search Engine Optimization) and paid ads.
- Platform experience: Gradually some services won the competition for most of our screen time due to their information ecosystems from news to entertainment - tailored for us by the most sophisticated algorithms available. For some time, most digital services have felt like the innovation has been halted.
Now, the web evolution is leading towards Cross-platform experiences, made possible by commercial data and digital ownership (enabled by blockchain). Combined with powerful LLMs (Large Language Models), the Data Economy is the sum of its parts. The next generation of the consumer internet is about data assets for cumulative value creation - and extensive innovative freedom for both the users and developers.
Data as a product
Web, as you remember it today, is just one kind of abstraction of data. To paint the picture of what data means as an asset, we need to zoom out to the academic basics of different types of economic goods:
Rival products
In economics, a good is said to be rivalrous or a rival if its consumption by one consumer prevents simultaneous consumption by others, or if consumption by one party reduces the ability of another party to consume it.
Most consumer goods (i.e., cars) belong to this category. The price of goods is usually altered by competition with rival product makers.
Non-rival products
Non-rivalrous goods are public goods that are consumed by people but whose supply is not affected by people's consumption. In other words, when an individual or a group of individuals use a particular good, the supply left for other people to use remains unchanged.
Most digitally streamed products (i.e., music) belong to this category. The price of goods and value for creators are altered by competition with rival service providers.
Anti-rival products
Anti-rivalry is a neologism suggested by Steven Weber. According to his definition, it is the opposite of a rival good. The more people share an anti-rival good, the more utility each person receives, and the more valuable the good becomes.
Most UGC data (user generated content) (i.e., social media posts, blog articles) today belongs to this group. Any given price for anti-rival goods rarely exist, yet the generated value can scale.
Data by default is an anti-rival product, so it scales in value when consumed. An open, decentralized data market enables IP (Intellectual Property) to belong to its creator/buyer, and data to become a cumulative value-creating asset class through fair market rules.
When created and consumed exclusively in a single centralized platform as a platform-owned private asset class, it creates inequality and concentration of value.
LLMs and Data economics
As internet is rapidly changing due to more powerful data tools available today, new online users may never experience the significance of search engines. The next generation is starting their web journey with tools that most existing users are trying to adopt: browsers implementing LLMs (Large Language Models) as native search and browsing tools are giving us a glimpse of the reality of the internet with less user exposure to the Web Protocol itself.
As a case example: Arc browser is one of the first global browsers to adopt the feature of "letting browser browse the web for you". The future is about LLM optimization, not SEO. Information-providing websites powered only by ad revenue (news and blogs for example) are on the way to inevitable destruction.
Three stages of data economy, suggested by STRGL:
1 Passive contribution / Offchain
In the first phase of the data economy, most services have data in non-commercial formats. This leads to digital information being widely exposed to cross-LLM training through unlawful scraping, and the actual data owners are not compensated for the information used for the next generation of AI services. This is where most digital services stand today, and most of the existing data is already available through AI chats without entering any additional services. Example: NY Times versus OpenAI and Microsoft.
2. Legal enforcement / Offchain
In the second phase of the data economy, some large services and corporations take legal action against LLM companies. They aim to get into contractual relationships with the AI providers on scraping and using the data they own and get compensated for the information usage. This course of action will only be available to the most powerful players with resources for unique negotiations. Example: OpenAI and Le Monde partnership.
3. Open data market / Onchain
STRGL believes the third phase of the data economy evolution is the optimal outcome for the next generation of the internet. In this phase, services are beginning to enforce their commercial digital IP through the next generation of blockchain-based Internet protocols.
This means tokenizing assets like news articles, blog posts, audio and video files, and social media content will offer AI tools easy ways to integrate themselves with smart contracts and compensate the owners and writers through micro-payments.
Use case examples
Purchasing digital assets: Flight tickets
You’re going to buy flight tickets directly from AI chat in no time. Tickets are tokenized asset class, bought from cross-platform ticket protocol (smart contracts). The purchase happens through your favourite browser “search engine”, operating a data-currency exchange with dynamic UX. There will be no need to move across third-party platforms, as the platform data prices and other information will be brought directly to the frontend.
Selling digital assets: Academic article
You’re getting compensated for that valuable academic paper you submitted to the internet. When someone is searching for answers online, LLMs look for relevant data – and compensate the named contributor of optimal data source directly in micropayments. This happens through the same named data protocol process as in the flight ticket example above. Now as the data is in a commercial format, you become the seller.
This article is a short overview of STRGL’s perception of the changing digital landscape, based on our years of research and development activity in the domain of tokenized data economy. Through our extensive partnerships with institutions and blockchain native innovation, we’re staying true to our dedication to advancing the web to a fairer and more innovative landscape to benefit us all.
As the world is currently leaning towards digital currencies like Bitcoin and stablecoins, we can feel the pulse of this a historic shift towards the era of digital assets. Accelerating blockchain adoption will play a significant role in the future of currency, data and identity standards.
“Skate where the puck is going.”
