Over twenty years ago, at Yahoo!, I witnessed the birth of digital platforms. We thought connecting advertisers with publishers was revolutionary. It was just the beginning.
The evolution of platforms tells an interesting story about value creation. From simple matchmaking to data-driven intelligence, to AI-powered orchestration. Each wave fundamentally changed how platforms create and capture value.
Think of it this way:
- Wave 1: eBay connecting buyers with sellers
- Wave 2: Amazon using data to power recommendations
- Wave 3: ChatGPT creating entirely new capabilities
I’ve not just witnessed these waves – I’ve helped shape them firsthand. At Yahoo!, processing billions in ad transactions. At PubMatic, connecting thousands of publishers with advertisers. At dunnhumby, orchestrating retail media networks and some of the world biggest retail data platforms. Let’s explore what these patterns tell us about the future of platforms.
Wave 1: The Matchmaking Era
The first wave focused on reducing friction. Two fundamental benefits drove platform adoption: search cost reduction and transaction cost reduction. Finding matches became easier. Executing transactions became simpler. Platforms made both cheaper.
At Yahoo!, we built one of the first scaled advertising platforms. The mechanics were straightforward: match advertisers with available ad inventory. More publishers attracted more advertisers. More advertisers attracted more publishers. A classic network effect.
Three elements defined this era:
Simple Network Effects Growth was multiplicative, not additive. Take PubMatic as an example: each new premium publisher might attract 50 new advertisers, each new advertiser increasing publisher yield by 0.5%. Every new participant multiplied value. More publishers, more advertisers, more spend… the flywheel spun faster. That’s platform magic.
Transaction-Based Value Revenue came from enabling transactions. Take rates were simple – a share of transaction value. At Yahoo!, this meant processing billions in annual advertising revenues – 20 years ago!
Rule-Based Intelligence Matching was manual or rule-based. Google’s introduction of eCPM was revolutionary – creating a common currency for different ad formats and pricing models. Yet even this innovation was fundamentally rule-based: smart arithmetic rather than true intelligence.
This model created billion-dollar businesses. But it had limitations. Market inefficiencies persisted. Value leaked. Most importantly, platforms weren’t learning from their transactions.
The real value was hiding in plain sight: the data these transactions generated.
Wave 2: The Data Advantage
Wave 1 reduced costs. Wave 2 created entirely new value. The shift wasn’t about efficiency – it was about possibility.
At dunnhumby, we created and operated some of the world biggest and most successful three-sided platforms (Walmart, Tesco, for example): shoppers buying products, retailers maximizing sales, CPG manufacturers selling more through retailers. The magic? Data from 800 million shoppers and hundred of thousands of products optimizing all connections. Each transaction made the platform smarter about consumer behavior. Retailers optimized their offering. CPG manufacturers targeted their promotions better.
From Transactions to Intelligence The game changed. Purchase data predicted future behavior. Retailers learned what to stock. CPG manufacturers discovered what would sell. Shoppers received offers they wanted. When I was at dunnhumby, this approach helped reignite revenue growth to whole new levels. The value wasn’t in the transaction – it was in the intelligence.
Network Effects 2.0 Data created multi-dimensional network effects. More shoppers generated more data. Better data helped retailers optimize. Optimization attracted CPG investment. Investment improved shopper experiences. Better experiences attracted more shoppers. Each party made the platform more valuable for others.
The Platform Intelligence Layer Platforms became intelligence engines, answering complex questions:
- What products should each store stock?
- Which promotions drive the most value? Which one are loss making?
- How can retailers and CPGs collaborate better?
- What’s the optimal price point per store, per product, per time of the day?
A new form of lock-in emerged: intelligence effects. The more each party used the platform, the more valuable it became for everyone. Once you start, you can’t stop.
But even this sophisticated learning system was just a preview of what was coming.
Wave 3: The AI Amplification
Wave 1 reduced costs. Wave 2 created value from existing interactions. Wave 3 is creating capabilities that never existed before.
Previous waves create or optimized connections. AI platforms do something different: they create new capabilities on demand.
From Optimization to Creation ChatGPT and GitHub Copilot showcase this shift. They don’t just optimize workflows – they create new ones. At dunnhumby, we built specific analytics features. Today’s AI platforms create capabilities on the fly, adapting to each unique request.
The New Network Effect Every interaction expands platform capabilities. One user teaches the agent a new task – that capability becomes available to everyone. Platforms grow in capability, not just size.
Value Creation at Scale Traditional platforms replicated services to more users. AI platforms expand capabilities for all users. At Yahoo!, scaling meant more advertisers using the same features. Today’s platforms create new features as they scale.
The Platform Becomes the Product Here’s the fundamental shift: platforms are becoming active participants in value creation. GitHub Copilot isn’t just connecting developers with code – it’s creating code. The platform transforms from repository to development partner.
Take Adobe Creative Cloud. Traditional platforms offered tools and assets – templates, fonts, stock images. Today’s AI-enabled platforms generate images, suggest improvements, automate editing tasks, create campaign variations. The platform becomes an intelligent creative collaborator.
This changes everything about platform economics:
- Business models must account for creation costs
- Value capture must reflect capability creation
- Network effects balance user growth with capability growth
We’re just beginning to understand the implications. But one thing is clear: AI isn’t just another feature for platforms – it’s a fundamental reimagining of what a platform can be. If you just think that AI is an efficiency generating feature, you are simply missing the forest for the trees. And this will be an expensive mistake.
How is your organization thinking about these platform evolution waves? Still optimizing transactions, or ready to create new capabilities? What’s your forest? Share your thoughts in the comments.
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