the end of code part two

I did this presentation in June last year at a conference in India and I really wanted to share it so here we go: from code to data, or how users today are training products like we train dogs to perform specific tasks. It’s a long narrative story so I am going to spread it across a few weeks’ posts. (Read The End of Code Part One here)

A fundamental platform shift: data network effects

As discussed previously, today we are undergoing one of the largest platform shifts in a generation as applications move to the cloud and are consumed on iPhones, Echoes and Teslas, while being built on new stacks and fueled by AI and data. And with it, the way tech companies conduct business is also changing .... and with it the employee skillset necessary for tech companies to stay ahead is also being rapidly disrupted.

​But before we look at these implications, let’s have a quick look at how the tech business models are evolving from network effects to data network effects by inserting AI-driven systems of intelligence in the current business models.

Traditional tech business models

​To establish themselves as powerhouses companies like google, facebook, amazon, or microsoft all were built on economies of scale and network effects.

• Economies of scale: the bigger a company is the more operating leverage thus lower costs. SaaS and Cloud Services have strong economies of scale, they can scale revenue and customer base while keeping the product’s core engineering flat
• Network effects: a product or service has “network effects” if each additional user of the product accrues more value to every other user. Slack, WhatsApp, Facebook are good examples of strong network effects. Same for windows, iOS and Android because the more users the more apps are built on top of it
• IP/Trade Secrets: most tech companies always start with proprietary software and/or methods - for example new solutions to hard technical problems, new inventions, techniques, and then patents to protect the new intellectual property
• High switching costs: if a customer is using your product, it may be difficult to switch to a competitor (example the “walled gardens” of windows and office)
  
• ​Brand and customer loyalty: a strong brand can be a good defense: with each positive interaction between the product and the customer, the brand advantage gets stronger over time ( ... only to quickly evaporate if customers lose trust in the product, example Chipotle)
  

Going full stack

Today the market is favoring “full stack” companies, i.e. SaaS offerings that offer application logic, middleware, and databases combined.

Technology is becoming an invisible component of a complete solution (Example no one cares what database backs your favorite mobile app as long as your food is delivered on time). In the consumer world, Apple made the integrated or full stack experience popular with the iPhone which seamlessly integrated hardware with software.

This integrated experience is coming to dominate enterprise software as well. Cloud and SaaS has made it possible to reach customers directly and in a cost-effective manner. As a result, customers are increasingly buying full stack technology in the form of SaaS applications instead of buying individual pieces of the tech stack and building their own apps.

Today’s SaaS stack: systems of engagement and systems of record

• Systems of record: At the bottom of the SaaS stack is usually a database on top of which an application is built. If the data and app power a critical business function, it becomes a “system of record.”
• Systems of engagement: Systems of engagement are the interfaces between users and the systems of record and can be powerful businesses because they control the end user interactions. In a multi-channel world, owning the system of engagement is most valuable if the business controls most of the end user engagement or is a cross-channel system that can reach the end-users wherever they are

The new tech business model: data network effects

At the core of data network effects are AI-driven systems of intelligence, which typically cross multiple data sets by inserting themselves in between systems of record and systems of engagement.

​Success is then achieved by using customer and market data to train and improve models that make the product better for all customers, which spins the flywheel of intelligence faster. Ultimately the product becomes tailored for each customer (that’s the “personalization” aspect we talked about in the intro).

To be continued!

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Let me know what you think! DM me @philippemora
​My name's phil mora and I blog about the things I love: fitness, hacking work, tech and anything holistic. 

Head of Product and VP Engineering at Sikka Software.
thinker, doer, designer, coder, leader.